Manufacturing-ready generative AI on Android with Vertex AI in Firebase

Posted by Thomas Ezan – Sr. Developer Relation Engineer (@lethargicpanda)

Gemini might help you construct and launch new consumer options that may enhance engagement and create personalised experiences to your customers.

The Vertex AI in Firebase SDK enables 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 turned usually accessible final October which suggests it is now prepared for manufacturing and it’s already utilized by many apps in Google Play.

Listed below 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 visitors is coming out of your genuine app operating on an genuine and untampered Android gadget.

To get began, add Firebase to your Android venture and allow the Play Integrity API to your app within the Google Play console. Again within the Firebase console, go to the App Verify part of your Firebase venture to register your app by offering its SHA-256 fingerprint.

Then, replace your Android venture’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 another Firebase SDK:

Firebase.initialize(context)
Firebase.appCheck.installAppCheckProviderFactory(
    PlayIntegrityAppCheckProviderFactory.getInstance(),
)

To reinforce the safety of your generative AI characteristic, you must 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 gives an additional layer of safety for these assets as nicely.

As soon as App Verify is enforced, you’ll have the ability to monitor your app’s requests within the Firebase console.

You may 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 fashions web page for particulars.

Due to this, as an alternative of hardcoding the mannequin identify 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 select up a brand new model.

You outline parameters that you simply wish to management (like mannequin identify) 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’ll be able to change the worth of those parameters at any time. Your app will robotically fetch the brand new worth.

Here is 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 identify
val modelName = remoteConfig.getString("model_name")

Learn extra about utilizing Distant Config with Vertex AI in Firebase.

Collect consumer suggestions to judge affect

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’ll be able to incorporate interactive parts equivalent to thumb-up and thumb-down buttons and detailed suggestions kinds inside the consumer interface. The Materials Icons in Compose bundle gives prepared to make use of icons that can assist you implement it.

You may 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 occasion logging capabilities within the Firebase documentation.

Consumer privateness and accountable AI

If you use Vertex AI in Firebase for inference, you have got 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 essential to be clear together with your customers once they’re participating with generative AI know-how. It is best to spotlight the opportunity 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 may be taught extra about how Google is approaching Generative AI responsibly within the Google Cloud documentation.