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Gemini can assist you construct and launch new consumer options that can increase engagement and create personalised experiences on your customers.
The Vertex AI in Firebase SDK allows you to entry Google’s Gemini Cloud fashions (like Gemini 1.5 Flash and Gemini 1.5 Professional) and add GenAI capabilities to your Android app. It grew to become typically accessible final October which suggests it is now prepared for manufacturing and it’s already utilized by many apps in Google Play.
Listed here are suggestions for a profitable deployment to manufacturing.
Implement App Verify to stop API abuse
When utilizing the Vertex AI in Firebase API it’s essential to implement sturdy safety measures to stop unauthorized entry and misuse.
Firebase App Verify helps defend backend assets (like Vertex AI in Firebase, Cloud Capabilities for Firebase, and even your individual customized backend) from abuse. It does this by testifying that incoming site visitors is coming out of your genuine app operating on an genuine and untampered Android system.
customers entry your backend assets
To get began, add Firebase to your Android project and allow the Play Integrity API on your app within the Google Play console. Again within the Firebase console, go to the App Verify part of your Firebase undertaking to register your app by offering its SHA-256 fingerprint.
Then, replace your Android undertaking’s Gradle dependencies with the App Verify library for Android:
dependencies {
// BoM for the Firebase platform
implementation(platform("com.google.firebase:firebase-bom:33.7.0"))
// Dependency for App Verify
implementation("com.google.firebase:firebase-appcheck-playintegrity")
}
Lastly, in your Kotlin code, initialize App Verify earlier than utilizing some other Firebase SDK:
Firebase.initialize(context)
Firebase.appCheck.installAppCheckProviderFactory(
PlayIntegrityAppCheckProviderFactory.getInstance(),
)
To boost the safety of your generative AI characteristic, it is best to implement and implement App Verify earlier than releasing your app to manufacturing. Moreover, in case your app makes use of different Firebase providers like Firebase Authentication, Firestore, or Cloud Capabilities, App Verify supplies an additional layer of safety for these assets as effectively.
As soon as App Verify is enforced, you’ll have the ability to monitor your app’s requests within the Firebase console.
You possibly can be taught extra about App Verify on Android within the Firebase documentation.
Use Distant Config for server-controlled configuration
The generative AI panorama evolves rapidly. Each few months, new Gemini mannequin iterations change into accessible and a few fashions are eliminated. See the Vertex AI in Firebase Gemini models page for particulars.
Due to this, as a substitute of hardcoding the mannequin title in your app, we advocate utilizing a server-controlled variable utilizing Firebase Remote Config. This lets you dynamically replace the mannequin your app makes use of with out having to deploy a brand new model of your app or require your customers to select up a brand new model.
You outline parameters that you simply need to management (like mannequin title) utilizing the Firebase console. Then, you add these parameters into your app, together with default “fallback” values for every parameter. Again within the Firebase console, you may change the worth of those parameters at any time. Your app will robotically fetch the brand new worth.
Here is the best way to implement Distant Config in your app:
// Initialize the distant configuration by defining the refresh time val remoteConfig: FirebaseRemoteConfig = Firebase.remoteConfig val configSettings = remoteConfigSettings { minimumFetchIntervalInSeconds = 3600 } remoteConfig.setConfigSettingsAsync(configSettings) // Set default values outlined in your app assets remoteConfig.setDefaultsAsync(R.xml.remote_config_defaults) // Load the mannequin title val modelName = remoteConfig.getString("model_name")
Learn extra about using Remote Config with Vertex AI in Firebase.
Collect consumer suggestions to judge influence
As you roll out your AI-enabled characteristic to manufacturing, it is vital to construct suggestions mechanisms into your product and permit customers to simply sign whether or not the AI output was useful, correct, or related. For instance, you may incorporate interactive parts reminiscent of thumb-up and thumb-down buttons and detailed suggestions types inside the consumer interface. The Material Icons in Compose package deal supplies prepared to make use of icons that can assist you implement it.
You possibly can simply monitor the consumer interplay with these parts as customized analytics occasions through the use of Google Analytics logEvent() perform:
Row {
Button (
onClick = {
firebaseAnalytics.logEvent("model_response_feedback") {
param("suggestions", "thumb_up")
}
}
) {
Icon(Icons.Default.ThumbUp, contentDescription = "Thumb up")
},
Button (
onClick = {
firebaseAnalytics.logEvent("model_response_feedback") {
param("suggestions", "thumb_down")
}
}
) {
Icon(Icons.Default.ThumbDown, contentDescription = "Thumb down")
}
}
Be taught extra about Google Analytics and its event logging capabilities within the Firebase documentation.
Person privateness and accountable AI
Once you use Vertex AI in Firebase for inference, you might have the assure that the info despatched to Google gained’t be utilized by Google to coach AI fashions (see Vertex AI documentation for particulars).
It is also necessary to be clear along with your customers once they’re partaking with generative AI know-how. It is best to spotlight the potential of surprising mannequin habits.
Lastly, customers ought to have management inside your app over how their exercise associated to AI mannequin interactions is saved and deleted.
You possibly can be taught extra about how Google is approaching Generative AI responsibly within the Google Cloud documentation.
