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    Get started with the Gemini API using the Firebase AI Logic SDKs

    This guide shows you how to get started making calls to the Gemini API directly from your app using the Firebase AI Logic client SDKs for your chosen platform.

    You can also use this guide to get started with accessing Imagen models using the Firebase AI Logic  SDKs.

    Prerequisites

    Swift

    This guide assumes that you're familiar with using Xcode to develop apps for Apple platforms (like iOS).

    Make sure that your development environment and Apple platforms app meet these requirements:

    • Xcode 16.2 or higher
    • Your app targets iOS 15 or higher, or macOS 12 or higher

    Kotlin

    This guide assumes that you're familiar with using Android Studio to develop apps for Android.

    Make sure that your development environment and Android app meet these requirements:

    • Android Studio (latest version)
    • Your app targets API level 21 or higher

    Java

    This guide assumes that you're familiar with using Android Studio to develop apps for Android.

    Make sure that your development environment and Android app meet these requirements:

    • Android Studio (latest version)
    • Your app targets API level 21 or higher

    Web

    This guide assumes that you're familiar with using JavaScript to develop web apps. This guide is framework-independent.

    Make sure that your development environment and web app meet these requirements:

    • (Optional) Node.js
    • Modern web browser

    Dart

    This guide assumes that you're familiar with developing apps with Flutter.

    Make sure that your development environment and Flutter app meet these requirements:

    • Dart 3.2.0+

    Unity

    This guide assumes that you're familiar with developing games with Unity.

    Make sure that your development environment and Unity game meet these requirements:

    • Unity Editor 2021 LTS or newer

    Check out helpful resources

    Swift

    Try out the quickstart app

    Use the quickstart app to try out the SDK quickly and see a complete implementation of various use cases. Or use the quickstart app if don't have your own Apple platforms app. To use the quickstart app, you'll need to connect it to a Firebase project .

    Go to the quickstart app

    Watch a video tutorial

    This video demonstrates how to get started with Firebase AI Logic by building a real-world AI-powered meal planning app that generates recipes from a text prompt.

    You can also download and explore the codebase for the app in the video.

    View the codebase for the video's app



    Kotlin

    Try out the quickstart app

    Use the quickstart app to try out the SDK quickly and see a complete implementation of various use cases. Or use the quickstart app if don't have your own Android app. To use the quickstart app, you'll need to connect it to a Firebase project .

    Go to the quickstart app

    Watch a video tutorial

    This video demonstrates how to get started with Firebase AI Logic by building a real-world AI-powered meal planning app that generates recipes from a text prompt.

    You can also download and explore the codebase for the app in the video.

    View the codebase for the video's app



    Java

    Try out the quickstart app

    Use the quickstart app to try out the SDK quickly and see a complete implementation of various use cases. Or use the quickstart app if don't have your own Android app. To use the quickstart app, you'll need to connect it to a Firebase project .

    Go to the quickstart app

    Watch a video tutorial

    This video demonstrates how to get started with Firebase AI Logic by building a real-world AI-powered meal planning app that generates recipes from a text prompt. *

    You can also download and explore the codebase for the app in the video.

    View the codebase for the video's app

    * This video and its app are in Kotlin, but they can still help Java developers understand the basics about how to get started with Firebase AI Logic .

    Web

    Try out the quickstart app

    Use the quickstart app to try out the SDK quickly and see a complete implementation of various use cases. Or use the quickstart app if don't have your own web app. To use the quickstart app, you'll need to connect it to a Firebase project .

    Go to the quickstart app

    Dart

    Try out the quickstart app

    Use the quickstart app to try out the SDK quickly and see a complete implementation of various use cases. Or use the quickstart app if don't have your own Flutter app. To use the quickstart app, you'll need to connect it to a Firebase project .

    Go to the quickstart app

    Watch a video tutorial

    This video demonstrates how to get started with Firebase AI Logic by building a real-world AI-powered meal planning app that generates recipes from a text prompt.

    You can also download and explore the codebase for the app in the video.

    View the codebase for the video's app



    Unity

    Try out the quickstart app

    Use the quickstart app to try out the SDK quickly and see a complete implementation of various use cases. Or use the quickstart app if don't have your own Unity game. To use the quickstart app, you'll need to connect it to a Firebase project .

    Go to the quickstart app

    Step 1: Set up a Firebase project and connect your app

    1. Sign into the Firebase console , and then select your Firebase project.

      Don't already have a Firebase project?

      If you don't already have a Firebase project, click the button to create a new Firebase project, and then use either of the following options:

      • Option 1: Create a wholly new Firebase project (and its underlying Google Cloud project automatically) by entering a new project name in the first step of the workflow.

      • Option 2: "Add Firebase" to an existing Google Cloud project by clicking Add Firebase to Google Cloud project(at bottom of page). In the first step of the workflow, start entering the project nameof the existing project, and then select the project from the displayed list.

      Complete the remaining steps of the on-screen workflow to create a Firebase project. Note that when prompted, you do not need to set up Google Analytics to use the Firebase AI Logic  SDKs.

  • In the Firebase console, go to the Firebase AI Logic page .

  • Click Get startedto launch a guided workflow that helps you set up the required APIs and resources for your project.

  • Select the " Gemini API " provider that you'd like to use with the Firebase AI Logic  SDKs. Gemini Developer API is recommended for first-time users. You can always add billing or set up Vertex AI Gemini API later, if you'd like.

    • Gemini Developer API — billing optional (available on the no-cost Spark pricing plan, and you can upgrade later if desired)
      The console will enable the required APIs and create a Gemini API key in your project.
      Do notadd this Gemini API key into your app's codebase. Learn more.

    • Vertex AI Gemini API — billing required (requires the pay-as-you-go Blaze pricing plan)
      The console will help you set up billing and enable the required APIs in your project.

  • If prompted in the console's workflow, follow the on-screen instructions to register your app and connect it to Firebase.

  • Continue to the next step in this guide to add the SDK to your app.

    • Step 2: Add the SDK

    With your Firebase project set up and your app connected to Firebase (see previous step), you can now add the Firebase AI Logic  SDK to your app.

    Swift

    Use Swift Package Manager to install and manage Firebase dependencies.

    The Firebase AI Logic library provides access to the APIs for interacting with Gemini and Imagen models. The library is included as part of the Firebase SDK for Apple platforms ( firebase-ios-sdk ).

    If you're already using Firebase, then make sure your Firebase package is v11.13.0 or later.

    1. In Xcode, with your app project open, navigate to File > Add Package Dependencies.

    2. When prompted, add the Firebase Apple platforms SDK repository:

       https://github.com/firebase/firebase-ios-sdk

    3. Select the latest SDK version.

    4. Select the FirebaseAI library.

    When finished, Xcode will automatically begin resolving and downloading your dependencies in the background.

    Kotlin

    The Firebase AI Logic  SDK for Android ( firebase-ai ) provides access to the APIs for interacting with Gemini and Imagen models.

    In your module (app-level) Gradle file(like <project>/<app-module>/build.gradle.kts ), add the dependency for the Firebase AI Logic library for Android. We recommend using the Firebase Android BoM to control library versioning.

     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" 
 ) 
 }

    By using the Firebase Android BoM , your app will always use compatible versions of Firebase Android libraries.

    (Alternative)   Add Firebase library dependencies  without  using the BoM

    If you choose not to use the Firebase BoM , you must specify each Firebase library version in its dependency line.

    Note that if you use multiple Firebase libraries in your app, we strongly recommend using the BoM to manage library versions, which ensures that all versions are compatible.

     dependencies 
  
 { 
  
 // Add the dependency for the Firebase AI Logic 
library 
  
 // When NOT using the BoM 
, you must specify versions in Firebase library dependencies 
  
 implementation 
 ( 
 "com.google.firebase:firebase-ai:17.2.0" 
 ) 
 }

    Java

    The Firebase AI Logic  SDK for Android ( firebase-ai ) provides access to the APIs for interacting with Gemini and Imagen models.

    In your module (app-level) Gradle file(like <project>/<app-module>/build.gradle.kts ), add the dependency for the Firebase AI Logic library for Android. We recommend using the Firebase Android BoM to control library versioning.

    For Java, you need to add two additional libraries.

     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" 
 ) 
  
 // 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" 
 ) 
 }

    By using the Firebase Android BoM , your app will always use compatible versions of Firebase Android libraries.

    (Alternative)   Add Firebase library dependencies  without  using the BoM

    If you choose not to use the Firebase BoM , you must specify each Firebase library version in its dependency line.

    Note that if you use multiple Firebase libraries in your app, we strongly recommend using the BoM to manage library versions, which ensures that all versions are compatible.

     dependencies 
  
 { 
  
 // Add the dependency for the Firebase AI Logic 
library 
  
 // When NOT using the BoM 
, you must specify versions in Firebase library dependencies 
  
 implementation 
 ( 
 "com.google.firebase:firebase-ai:17.2.0" 
 ) 
 }

    Web

    The Firebase AI Logic library provides access to the APIs for interacting with Gemini and Imagen models. The library is included as part of the Firebase JavaScript SDK for Web.

    1. Install the Firebase JS SDK for Web using npm:

        npm install firebase

    2. Initialize Firebase in your app:

        import 
  
 { 
  
 initializeApp 
  
 } 
  
 from 
  
 "firebase/app" 
 ; 
 // TODO(developer) Replace the following with your app's Firebase configuration 
 // See: https://firebase.google.com/docs/web/learn-more#config-object 
 const 
  
 firebaseConfig 
  
 = 
  
 { 
  
 // ... 
 }; 
 // Initialize FirebaseApp 
 const 
  
 firebaseApp 
  
 = 
  
 initializeApp 
 ( 
 firebaseConfig 
 );

    Dart

    The Firebase AI Logic plugin for Flutter ( firebase_ai ) provides access to the APIs for interacting with Gemini and Imagen models.

    1. From your Flutter project directory, run the following command to install the core plugin and the Firebase AI Logic plugin:

       flutter  
pub  
add  
firebase_core  
firebase_ai

    2. In your lib/main.dart file, import the Firebase core plugin, the Firebase AI Logic plugin, and the configuration file you generated earlier:

        import 
  
 'package:firebase_core/firebase_core.dart' 
 ; 
 import 
  
 'package:firebase_ai/firebase_ai.dart' 
 ; 
 import 
  
 'firebase_options.dart' 
 ;

    3. Also in your lib/main.dart file, initialize Firebase using the DefaultFirebaseOptions object exported by the configuration file:

        await 
  
 Firebase 
 . 
 initializeApp 
 ( 
  
 options: 
  
 DefaultFirebaseOptions 
 . 
 currentPlatform 
 , 
 );

    4. Rebuild your Flutter application:

       flutter  
run

    Unity

    1. Download the Firebase Unity SDK , then extract the SDK somewhere convenient.

      The Firebase Unity SDK is not platform-specific.

    2. In your open Unity project, navigate to Assets> Import Package> Custom Package.

    3. From the extracted SDK, select the FirebaseAI package.

    4. In the Import Unity Package window, click Import.

    5. Back in the Firebase console, in the setup workflow, click Next.

    Step 3: Initialize the service and create a model instance

    Click your Gemini API provider to view provider-specific content and code on this page.

    Before sending a prompt to a Gemini model, initialize the service for your chosen API provider and create a GenerativeModel instance.

    Swift

      import 
  
 FirebaseAI 
 // Initialize the Gemini Developer API backend service 
 let 
  
 ai 
  
 = 
  
 FirebaseAI 
 . 
 firebaseAI 
 ( 
 backend 
 : 
  
 . 
 googleAI 
 ()) 
 // Create a `GenerativeModel` instance with a model that supports your use case 
 let 
  
 model 
  
 = 
  
 ai 
 . 
 generativeModel 
 ( 
 modelName 
 : 
  
 "gemini-2.5-flash" 
 )

    Kotlin 

      // Initialize the Gemini Developer API backend service 
 // Create a `GenerativeModel` instance with a model that supports your use case 
 val 
  
 model 
  
 = 
  
 Firebase 
 . 
 ai 
 ( 
 backend 
  
 = 
  
 GenerativeBackend 
 . 
 googleAI 
 ()) 
  
 . 
 generativeModel 
 ( 
 "gemini-2.5-flash" 
 )

    Java 

      // Initialize the Gemini Developer API backend service 
 // Create a `GenerativeModel` instance with a model that supports your use case 
 GenerativeModel 
  
 ai 
  
 = 
  
 FirebaseAI 
 . 
 getInstance 
 ( 
 GenerativeBackend 
 . 
 googleAI 
 ()) 
  
 . 
 generativeModel 
 ( 
 "gemini-2.5-flash" 
 ); 
 // Use the GenerativeModelFutures Java compatibility layer which offers 
 // support for ListenableFuture and Publisher APIs 
 GenerativeModelFutures 
  
 model 
  
 = 
  
 GenerativeModelFutures 
 . 
 from 
 ( 
 ai 
 );

    Web 

      import 
  
 { 
  
 initializeApp 
  
 } 
  
 from 
  
 "firebase/app" 
 ; 
  import 
  
 { 
  
 getAI 
 , 
  
 getGenerativeModel 
 , 
  
 GoogleAIBackend 
  
 } 
  
 from 
  
 "firebase/ai" 
 ; 
 // TODO(developer) Replace the following with your app's Firebase configuration 
 // See: https://firebase.google.com/docs/web/learn-more#config-object 
 const 
  
 firebaseConfig 
  
 = 
  
 { 
  
 // ... 
 }; 
 // Initialize FirebaseApp 
 const 
  
 firebaseApp 
  
 = 
  
 initializeApp 
 ( 
 firebaseConfig 
 ); 
  // Initialize the Gemini Developer API backend service 
 const 
  
 ai 
  
 = 
  
 getAI 
 ( 
 firebaseApp 
 , 
  
 { 
  
 backend 
 : 
  
 new 
  
 GoogleAIBackend 
 () 
  
 }); 
 // Create a `GenerativeModel` instance with a model that supports your use case 
 const 
  
 model 
  
 = 
  
 getGenerativeModel 
 ( 
 ai 
 , 
  
 { 
  
 model 
 : 
  
 "gemini-2.5-flash" 
  
 });

    Dart 

      import 
  
 'package:firebase_ai/firebase_ai.dart' 
 ; 
 import 
  
 'package:firebase_core/firebase_core.dart' 
 ; 
 import 
  
 'firebase_options.dart' 
 ; 
 // Initialize FirebaseApp 
 await 
  
 Firebase 
 . 
 initializeApp 
 ( 
  
 options: 
  
 DefaultFirebaseOptions 
 . 
 currentPlatform 
 , 
 ); 
  // Initialize the Gemini Developer API backend service 
 // Create a `GenerativeModel` instance with a model that supports your use case 
 final 
  
 model 
  
 = 
  
 FirebaseAI 
 . 
 googleAI 
 (). 
 generativeModel 
 ( 
 model: 
  
 'gemini-2.5-flash' 
 );

    Unity

      using 
  
 Firebase 
 ; 
 using 
  
 Firebase.AI 
 ; 
 // Initialize the Gemini Developer API backend service 
 var 
  
 ai 
  
 = 
  
 FirebaseAI 
 . 
 GetInstance 
 ( 
 FirebaseAI 
 . 
 Backend 
 . 
 GoogleAI 
 ()); 
 // Create a `GenerativeModel` instance with a model that supports your use case 
 var 
  
 model 
  
 = 
  
 ai 
 . 
 GetGenerativeModel 
 ( 
 modelName 
 : 
  
 "gemini-2.5-flash" 
 );

    Note that depending on the capability you're using, you might not always create a GenerativeModel instance.

    Also, after you finish this getting started guide, learn how to choose a model for your use case and app.

    Step 4: Send a prompt request to a model

    You're now set up to send a prompt request to a Gemini model.

    You can use generateContent() to generate text from a prompt that contains text:

    Swift

      import 
  
 FirebaseAI 
 // Initialize the Gemini Developer API backend service 
 let 
  
 ai 
  
 = 
  
 FirebaseAI 
 . 
 firebaseAI 
 ( 
 backend 
 : 
  
 . 
 googleAI 
 ()) 
 // Create a `GenerativeModel` instance with a model that supports your use case 
 let 
  
 model 
  
 = 
  
 ai 
 . 
 generativeModel 
 ( 
 modelName 
 : 
  
 "gemini-2.5-flash" 
 ) 
  // Provide a prompt that contains text 
 let 
  
 prompt 
  
 = 
  
 "Write a story about a magic backpack." 
 // To generate text output, call generateContent with the text input 
 let 
  
 response 
  
 = 
  
 try 
  
 await 
  
 model 
 . 
 generateContent 
 ( 
 prompt 
 ) 
 print 
 ( 
 response 
 . 
 text 
  
 ?? 
  
 "No text in response." 
 )

    Kotlin

    For Kotlin, the methods in this SDK are suspend functions and need to be called from a Coroutine scope . 
      // Initialize the Gemini Developer API backend service 
 // Create a `GenerativeModel` instance with a model that supports your use case 
 val 
  
 model 
  
 = 
  
 Firebase 
 . 
 ai 
 ( 
 backend 
  
 = 
  
 GenerativeBackend 
 . 
 googleAI 
 ()) 
  
 . 
 generativeModel 
 ( 
 "gemini-2.5-flash" 
 ) 
  // Provide a prompt that contains text 
 val 
  
 prompt 
  
 = 
  
 "Write a story about a magic backpack." 
 // To generate text output, call generateContent with the text input 
 val 
  
 response 
  
 = 
  
 generativeModel 
 . 
 generateContent 
 ( 
 prompt 
 ) 
 print 
 ( 
 response 
 . 
 text 
 )

    Java

    For Java, the methods in this SDK return a ListenableFuture . 
      // Initialize the Gemini Developer API backend service 
 // Create a `GenerativeModel` instance with a model that supports your use case 
 GenerativeModel 
  
 ai 
  
 = 
  
 FirebaseAI 
 . 
 getInstance 
 ( 
 GenerativeBackend 
 . 
 googleAI 
 ()) 
  
 . 
 generativeModel 
 ( 
 "gemini-2.5-flash" 
 ); 
 // Use the GenerativeModelFutures Java compatibility layer which offers 
 // support for ListenableFuture and Publisher APIs 
 GenerativeModelFutures 
  
 model 
  
 = 
  
 GenerativeModelFutures 
 . 
 from 
 ( 
 ai 
 ); 
  // Provide a prompt that contains text 
 Content 
  
 prompt 
  
 = 
  
 new 
  
 Content 
 . 
 Builder 
 () 
  
 . 
 addText 
 ( 
 "Write a story about a magic backpack." 
 ) 
  
 . 
 build 
 (); 
 // To generate text output, call generateContent with the text input 
 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 
 );

    Web 

      import 
  
 { 
  
 initializeApp 
  
 } 
  
 from 
  
 "firebase/app" 
 ; 
  import 
  
 { 
  
 getAI 
 , 
  
 getGenerativeModel 
 , 
  
 GoogleAIBackend 
  
 } 
  
 from 
  
 "firebase/ai" 
 ; 
 // TODO(developer) Replace the following with your app's Firebase configuration 
 // See: https://firebase.google.com/docs/web/learn-more#config-object 
 const 
  
 firebaseConfig 
  
 = 
  
 { 
  
 // ... 
 }; 
 // Initialize FirebaseApp 
 const 
  
 firebaseApp 
  
 = 
  
 initializeApp 
 ( 
 firebaseConfig 
 ); 
  // Initialize the Gemini Developer API backend service 
 const 
  
 ai 
  
 = 
  
 getAI 
 ( 
 firebaseApp 
 , 
  
 { 
  
 backend 
 : 
  
 new 
  
 GoogleAIBackend 
 () 
  
 }); 
 // Create a `GenerativeModel` instance with a model that supports your use case 
 const 
  
 model 
  
 = 
  
 getGenerativeModel 
 ( 
 ai 
 , 
  
 { 
  
 model 
 : 
  
 "gemini-2.5-flash" 
  
 }); 
  // Wrap in an async function so you can use await 
 async 
  
 function 
  
 run 
 () 
  
 { 
  
 // Provide a prompt that contains text 
  
 const 
  
 prompt 
  
 = 
  
 "Write a story about a magic backpack." 
  
 // To generate text output, call generateContent with the text input 
  
 const 
  
 result 
  
 = 
  
 await 
  
 model 
 . 
 generateContent 
 ( 
 prompt 
 ); 
  
 const 
  
 response 
  
 = 
  
 result 
 . 
 response 
 ; 
  
 const 
  
 text 
  
 = 
  
 response 
 . 
 text 
 (); 
  
 console 
 . 
 log 
 ( 
 text 
 ); 
 } 
 run 
 ();

    Dart 

      import 
  
 'package:firebase_ai/firebase_ai.dart' 
 ; 
 import 
  
 'package:firebase_core/firebase_core.dart' 
 ; 
 import 
  
 'firebase_options.dart' 
 ; 
 // Initialize FirebaseApp 
 await 
  
 Firebase 
 . 
 initializeApp 
 ( 
  
 options: 
  
 DefaultFirebaseOptions 
 . 
 currentPlatform 
 , 
 ); 
  // Initialize the Gemini Developer API backend service 
 // Create a `GenerativeModel` instance with a model that supports your use case 
 final 
  
 model 
  
 = 
  
 FirebaseAI 
 . 
 googleAI 
 (). 
 generativeModel 
 ( 
 model: 
  
 'gemini-2.5-flash' 
 ); 
  // Provide a prompt that contains text 
 final 
  
 prompt 
  
 = 
  
 [ 
 Content 
 . 
 text 
 ( 
 'Write a story about a magic backpack.' 
 )]; 
 // To generate text output, call generateContent with the text input 
 final 
  
 response 
  
 = 
  
 await 
  
 model 
 . 
 generateContent 
 ( 
 prompt 
 ); 
 print 
 ( 
 response 
 . 
 text 
 );

    Unity

      using 
  
 Firebase 
 ; 
 using 
  
 Firebase.AI 
 ; 
 // Initialize the Gemini Developer API backend service 
 var 
  
 ai 
  
 = 
  
 FirebaseAI 
 . 
 GetInstance 
 ( 
 FirebaseAI 
 . 
 Backend 
 . 
 GoogleAI 
 ()); 
 // Create a `GenerativeModel` instance with a model that supports your use case 
 var 
  
 model 
  
 = 
  
 ai 
 . 
 GetGenerativeModel 
 ( 
 modelName 
 : 
  
 "gemini-2.5-flash" 
 ); 
  // Provide a prompt that contains text 
 var 
  
 prompt 
  
 = 
  
 "Write a story about a magic backpack." 
 ; 
 // To generate text output, call GenerateContentAsync with the text input 
 var 
  
 response 
  
 = 
  
 await 
  
 model 
 . 
 GenerateContentAsync 
 ( 
 prompt 
 ); 
 UnityEngine 
 . 
 Debug 
 . 
 Log 
 ( 
 response 
 . 
 Text 
  
 ?? 
  
 "No text in response." 
 );

    What else can you do?

    Learn more about the supported models

    Learn about the models available for various use cases and their quotas and pricing .

    Try out other capabilities

    Learn how to control content generation

    You can also experiment with prompts and model configurations and even get a generated code snippet using Google AI Studio .


    Give feedback about your experience with Firebase AI Logic


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