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Gemini may also help you construct and launch new consumer options that can increase engagement and create customized experiences in your customers.
The Vertex AI in Firebase SDK helps 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 usually accessible final October which implies it is now prepared for manufacturing and it’s already utilized by many apps in Google Play.
Listed here are ideas for a profitable deployment to manufacturing.
Implement App Test to stop API abuse
When utilizing the Vertex AI in Firebase API it’s essential to implement strong safety measures to stop unauthorized entry and misuse.
Firebase App Test helps shield backend sources (like Vertex AI in Firebase, Cloud Features for Firebase, and even your personal customized backend) from abuse. It does this by testifying that incoming visitors is coming out of your genuine app working on an genuine and untampered Android machine.

customers entry your backend sources
To get began, add Firebase to your Android mission and allow the Play Integrity API in your app within the Google Play console. Again within the Firebase console, go to the App Test part of your Firebase mission to register your app by offering its SHA-256 fingerprint.
Then, replace your Android mission’s Gradle dependencies with the App Test library for Android:
dependencies { // BoM for the Firebase platform implementation(platform("com.google.firebase:firebase-bom:33.7.0")) // Dependency for App Test implementation("com.google.firebase:firebase-appcheck-playintegrity") }
Lastly, in your Kotlin code, initialize App Test earlier than utilizing every other Firebase SDK:
Firebase.initialize(context) Firebase.appCheck.installAppCheckProviderFactory( PlayIntegrityAppCheckProviderFactory.getInstance(), )
To boost the safety of your generative AI characteristic, it’s best to implement and implement App Test earlier than releasing your app to manufacturing. Moreover, in case your app makes use of different Firebase companies like Firebase Authentication, Firestore, or Cloud Features, App Test supplies an additional layer of safety for these sources as nicely.
As soon as App Test is enforced, you’ll be capable of monitor your app’s requests within the Firebase console.

You possibly can be taught extra about App Test 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 turn out to be accessible and a few fashions are eliminated. See the Vertex AI in Firebase Gemini fashions web page for particulars.
Due to this, as a substitute of hardcoding the mannequin title in your app, we suggest utilizing a server-controlled variable utilizing Firebase Distant 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 choose up a brand new model.
You outline parameters that you just 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.
This is learn how 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 sources remoteConfig.setDefaultsAsync(R.xml.remote_config_defaults) // Load the mannequin title val modelName = remoteConfig.getString("model_name")
Learn extra about utilizing Distant Config with Vertex AI in Firebase.
Collect consumer suggestions to judge impression
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 corresponding to thumb-up and thumb-down buttons and detailed suggestions varieties throughout the consumer interface. The Materials Icons in Compose package deal supplies prepared to make use of icons that can assist you implement it.
You possibly can simply observe 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 occasion logging capabilities within the Firebase documentation.
Consumer privateness and accountable AI
Whenever you use Vertex AI in Firebase for inference, you could have the assure that the information despatched to Google gained’t be utilized by Google to coach AI fashions (see Vertex AI documentation for particulars).
It is also vital to be clear along with your customers after they’re participating with generative AI know-how. It’s best to spotlight the potential of surprising mannequin conduct.
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.