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    Migrate to the Firebase AI Logic SDKs from the Google AI client SDKs


    Go directly to migration instructions

    Why migrate to use the Firebase AI Logic  SDKs?

    You might have tried out an alternative set of mobile or web client SDKs that gave you access to the Gemini Developer API .

    Those client SDKs were not integrated into the robust Firebase ecosystem that offers critical services for mobile and web apps. They are now deprecated in favor of the Firebase AI Logic client SDKs, which can give you access to the Gemini Developer API .

    Security features for mobile and web apps

    For mobile and web apps, security is critical and requires special considerations because your code – including calls to the Gemini API – is running in an unprotected environment. You can use Firebase App Check to protect APIs from abuse by unauthorized clients.

    When you use Firebase App Check with Firebase AI Logic , you never add your Gemini API key for the Gemini Developer API directly into your mobile or web app's codebase. Instead, the Gemini API key stays on the server, unexposed to malicious actors.

    Ecosystem built for mobile and web apps

    Firebase is Google's platform for developing mobile and web apps. Using Firebase AI Logic means that your apps are in an ecosystem that's focused on the needs of full-stack apps and developers. For example:

    • Dynamically set run-time configurations or swap out values in your app (like a model name and version) without releasing a new app version using Firebase Remote Config .

    • Use Cloud Storage for Firebase to include large files in your multimodal requests (if you use the Vertex AI Gemini API ). The Cloud Storage client SDKs help you handle file uploads and downloads (even in poor network conditions) and offer more security for your end-users' data. Learn more in our solution guide about using Cloud Storage for Firebase .

    • Manage structured data using database SDKs built for mobile and web apps (like Cloud Firestore ).

    Migrate to the Firebase AI Logic  SDKs

    Overview of steps to migrate to the Firebase AI Logic  SDKs:

    • Step 1: Set up a new or existing Firebase project and connect your app to Firebase.

    • Step 2: Add the Firebase AI Logic  SDKs to your app.

    • Step 3: Update your imports and initialization in your app.

    • Step 4: Update your code depending on the features that you use.

    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.

        If you'd like, you can add Firebase to the project that was created behind the scenes when you created a Gemini API key in Google AI Studio .

      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 AI Services> AI Logic .

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

  • Select the Gemini Developer API . You can always set up and use the other API provider later, if you'd like.

    The console will enable the required APIs and create a new, dedicated Gemini API key in your project.
    Do notadd this new Gemini API key into your app's codebase. Learn more.

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

  • Continue in this migration guide to update the library and initialization in your app.

    • Step 2: Add the Firebase AI Logic  SDK to your app

    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 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 v12.5.0 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 FirebaseAILogic 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 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:34.13.0" 
 )) 
  
 // 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:17.12.0" 
 ) 
 }

    Java

    The Firebase AI Logic  SDK for Android ( firebase-ai ) provides access to the APIs for interacting with Gemini 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:34.13.0" 
 )) 
  
 // 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:17.12.0" 
 ) 
 }

    Web

    The Firebase AI Logic library provides access to the APIs for interacting with Gemini 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 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

    Support for Unity wasn't available from the Google AI client SDKs.

    Learn how to get started with the Firebase AI Logic  SDK for Unity .

    Remove the old SDK from your app

    After you've finished migrating your app (see the remaining sections in this guide), make sure to delete the old library.

    Swift

    Remove the old library:

    1. In Xcode, with your app project open, navigate to the Packages Dependenciespane.

    2. Select the generative-ai-swift package from the list of package dependencies.

    3. Click the - button from the bottom of the list and click Removeto confirm.

    Kotlin 

      dependencies 
  
 { 
  
  implementation 
 ( 
 "com.google.ai.client.generativeai:generativeai:VERSION" 
 ) 
 
 }

    Java 

      dependencies 
  
 { 
  
  implementation 
 ( 
 "com.google.ai.client.generativeai:generativeai:VERSION" 
 ) 
 
 }

    Web 

      // BEFORE 
 import 
  
 { 
  
 initializeApp 
  
 } 
  
 from 
  
 "firebase/app" 
 ; 
  import 
  
 { 
  
 GoogleGenerativeAI 
  
 } 
  
 from 
  
 "@google/generative-ai" 
 ;

    Dart

    Delete the old package: flutter pub remove google_generative_ai

    Unity

    Support for Unity wasn't available from Google AI client SDKs.

    Learn how to get started with the Firebase AI Logic  SDK for Unity .

    Step 3: Update your imports and initialization in your app

    Update your imports and how you initialize the Gemini Developer API backend service and create a GenerativeModel instance.

    Swift

     // BEFORE 
  import 
  
 GoogleGenerativeAI 
 let 
  
 model 
  
 = 
  
 GenerativeModel 
 ( 
 name 
 : 
  
 "MODEL_NAME" 
 , 
  
 apiKey 
 : 
  
 APIKey 
 . 
 default 
 ) 
 
 // AFTER 
 import 
  
 FirebaseAILogic 
 // 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-3.5-flash" 
 )

    Kotlin 

     // BEFORE 
  import 
  
 com.google.ai.client.generativeai.Chat 
 import 
  
 com.google.ai.client.generativeai.type.Content 
 import 
  
 com.google.ai.client.generativeai.java.GenerativeModuleFutures 
 
 ... 
  val 
  
 generativeModel 
  
 = 
  
 GenerativeModel 
 ( 
 modelName 
  
 = 
  
 "MODEL_NAME" 
 , 
  
 // Access your API key as a Build Configuration variable 
  
 apiKey 
  
 = 
  
 BuildConfig 
 . 
 apiKey 
 ) 
 
 // AFTER 
 import 
  
 com.google.firebase.Firebase 
 import 
  
 com.google.firebase.ai.ai 
 import 
  
 com.google.firebase.ai.type.GenerativeBackend 
 ... 
 // 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-3.5-flash" 
 )

    Java 

     // BEFORE 
  import 
  
 com.google.ai.client.generativeai.Chat 
 ; 
 import 
  
 com.google.ai.client.generativeai.type.Content 
 ; 
 import 
  
 com.google.ai.client.generativeai.java.GenerativeModuleFutures 
 ; 
 
 ... 
  GenerativeModel 
  
 gm 
  
 = 
  
 new 
  
 GenerativeModel 
 ( 
 "MODEL_NAME" 
 , 
  
 // Access your API key as a Build Configuration variable 
  
 BuildConfig 
 . 
 apiKey 
 ); 
 GenerativeModelFutures 
  
 model 
  
 = 
  
 GenerativeModelFutures 
 . 
 from 
 ( 
 gm 
 ); 
 
 // AFTER 
 import 
  
 com.google.firebase.ai.FirebaseAI 
 ; 
 import 
  
 com.google.firebase.ai.GenerativeModel 
 ; 
 import 
  
 com.google.firebase.ai.java.GenerativeModelFutures 
 ; 
 import 
  
 com.google.firebase.ai.type.GenerativeBackend 
 ; 
 ... 
 // 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-3.5-flash" 
 ); 
 // Use the GenerativeModelFutures Java compatibility layer which offers 
 // support for ListenableFuture and Publisher APIs 
 GenerativeModelFutures 
  
 model 
  
 = 
  
 GenerativeModelFutures 
 . 
 from 
 ( 
 ai 
 );

    Web 

     // BEFORE 
  import 
  
 { 
  
 GoogleGenerativeAI 
  
 } 
  
 from 
  
 "@google/generative-ai" 
 ; 
 // Fetch your API_KEY and access your API 
 const 
  
 API_KEY 
  
 = 
  
 "..." 
 ; 
 const 
  
 genAI 
  
 = 
  
 new 
  
 GoogleGenerativeAI 
 ( 
 API_KEY 
 ); 
 
 ... 
  const 
  
 model 
  
 = 
  
 genAI 
 . 
 getGenerativeModel 
 ({ 
  
 model 
 : 
  
 "MODEL_NAME" 
 }); 
 
 // AFTER 
 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-3.5-flash" 
  
 });

    Dart 

     // BEFORE 
  import 
  
 'package:google_generative_ai/google_generative_ai.dart' 
 ; 
 final 
  
 apiKey 
  
 = 
  
 Platform 
 . 
 environment 
 [ 
 'API_KEY' 
 ]; 
 if 
  
 ( 
 apiKey 
  
 == 
  
 null 
 ) 
  
 { 
 print 
 ( 
 'No 
 \$ 
 API_KEY environment variable' 
 ); 
 exit 
 ( 
 1 
 ); 
 } 
 final 
  
 model 
  
 = 
  
 GenerativeModel 
 ( 
 model: 
  
 'MODEL_NAME' 
 , 
  
 apiKey: 
  
 apiKey 
 ); 
 
 // AFTER 
 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-3.5-flash' 
 );

    Unity

    Support for Unity wasn't available from Google AI client SDKs.

    Learn how to get started with the Firebase AI Logic  SDK for Unity .

    Note that depending on the capability you're using, you might not always create a GenerativeModel instance. To stream input and output using the Gemini Live API , create a LiveModel instance.

    Step 4: Update code depending on the features that you use

    This step describes changes that may be required depending on which features you use.

    • The Firebase AI Logic client SDKs don't support code execution. If you use this feature, make sure to accommodate this in your app.

    • Review the following lists for any changes that you might need to make in your code to accommodate migrating to the Firebase AI Logic client SDKs.

    Required for all languages and platforms

    • Function calling
      If you implemented this feature, then you'll need to make updates to how you define your schema. We recommend reviewing the updated function calling guide to learn how to write your function declarations.

    • Generating structured output (like JSON) using responseSchema
      If you implemented this feature, then you'll need to make updates to how you define your schema. We recommend reviewing the new structured output guide to learn how to write JSON schemas.

    • Timeout

      • Changed the default timeout for requests to be 180 seconds.

    Required based on platform or language

    Swift

    • Enumerations

      • Replaced most enum types with struct s with static variables. This change allows more flexibility for evolving the API in a backward-compatible way. When using switch statements, you must now include a default: case to cover unknown or unhandled values, including new values that are added to the SDK in the future.

      • Renamed the BlockThreshold enumeration to HarmBlockThreshold ; this type is now a struct .

      • Removed unknown and unspecified cases from the following enumerations (now struct s): HarmCategory , HarmBlockThreshold , HarmProbability , BlockReason , and FinishReason .

      • Replaced the enumeration ModelContent.Part with a protocol named Part to allow new types to be added in a backward-compatible way. This change is described in greater detail in the Content partssection.

    • Content parts

      • Removed the ThrowingPartsRepresentable protocol, and simplified the initializers for ModelContent to avoid occasional compiler errors. Images that don't encode properly will still throw errors when being used in generateContent .

      • Replaced the ModelContent.Part cases with the following struct types conforming to the Part protocol:

        • .text to TextPart
        • .data to InlineDataPart
        • .fileData to FileDataPart
        • .functionCall to FunctionCallPart
        • .functionResponse to FunctionResponsePart

    • Harm category

      • Changed the HarmCategory to no longer be nested in the SafetySetting type. If you're referring to it as SafetySetting.HarmCategory , that can be replaced with HarmCategory .

    • Safety feedback

      • Removed the SafetyFeedback type, since it wasn't used in any of the responses.

    • Citation metadata

      • Renamed the citationSources property to citations in CitationMetadata .

    • Total billable characters

      • Changed the totalBillableCharacters property in CountTokensResponse to be optional to reflect situations where no characters are sent.

    • Candidate response

      • Renamed CandidateResponse to Candidate to match other platforms.

    • Generation configuration

      • Changed the public properties of GenerationConfig to internal . They all remain configurable in the initializer.

    Kotlin

    • Enumerations

      • Replaced enum classes and sealed classes with regular classes. This change allows more flexibility for evolving the API in a backward compatible way.

      • Renamed the BlockThreshold enumeration to HarmBlockThreshold .

      • Removed values from the following enumerations: HarmBlockThreshold , HarmProbability , HarmSeverity , BlockReason , and FinishReason .

    • Blob methods

      • Renamed all methods that included Blob as part of their name to use InlineData instead.

    • Safety settings

      • Changed the field method to be nullable.

    • Duration class

      • Removed all usages of Kotlin's Duration class, and replaced it with long . This change provides better interoperability with Java.

    • Citation metadata

      • Wrapped all the fields previously declared in CitationMetadata into a new class called Citation . Citations can be found in the list called citations in CitationMetadata . This change allows better alignment of types across platforms.

    • Count tokens

      • Changed the field totalBillableCharacters to be nullable.

    • Total billable characters

      • Changed the totalBillableCharacters property in CountTokensResponse to be optional to reflect situations where no characters are sent.

    • Instantiating a model

      • Moved the requestOptions parameter to the end of the parameter list to align with other platforms.

    Java

    • Enumerations

      • Replaced enum classes and sealed classes with regular classes. This change allows more flexibility for evolving the API in a backward compatible way.

      • Renamed the BlockThreshold enumeration to HarmBlockThreshold .

      • Removed values from the following enumerations: HarmBlockThreshold , HarmProbability , HarmSeverity , BlockReason , and FinishReason .

    • Blob methods

      • Renamed all methods that included Blob as part of their name to use InlineData instead.

    • Safety settings

      • Changed the field method to be nullable.

    • Duration class

      • Removed all usages of Kotlin's Duration class, and replaced it with long . This change provides better interoperability with Java.

    • Citation metadata

      • Wrapped all the fields previously declared in CitationMetadata into a new class called Citation . Citations can be found in the list called citations in CitationMetadata . This change allows better alignment of types across platforms.

    • Count tokens

      • Changed the field totalBillableCharacters to be nullable.

    • Total billable characters

      • Changed the totalBillableCharacters property in CountTokensResponse to be optional to reflect situations where no characters are sent.

    • Instantiating a model

      • Moved the requestOptions parameter to the end of the parameter list to align with other platforms.

    Web

    Note that the Google AI client SDK for JavaScript has had many changes since the time that the Firebase AI Logic client SDKs branched from it. The following list are some potential changes that you might need to consider as you migrate to the Firebase AI Logic client SDKs.

    • Enumerations

      • Removed values from the following enumerations: HarmCategory , BlockThreshold , HarmProbability , HarmSeverity , BlockReason , and FinishReason .

    • Block reason

      • Changed blockReason in PromptFeedback to be optional.

    • Search Grounding

      • Removed all usages of this feature, since it's not yet supported in the Firebase AI Logic  SDKs.

    • Errors

      • Removed all usages of GoogleGenerativeAIError , and optionally move to AIError .

    Dart

    • Enumerations

      • Removed values from the following enumerations: HarmCategory , HarmProbability , BlockReason , and FinishReason .

    • Data part

      • Renamed DataPart to InlineDataPart , and the static data function to inlineData to align with other platforms.

    • Request options

      • Removed RequestOptions since timeout wasn't functional. It will be re-added in the near future, but it will be moved to the GenerativeModel type to match other platforms.

    • Stop sequences

      • Changed the stopSequences parameter in GenerationConfig to be optional and to default to null instead of an empty array.

    • Citations

      • Renamed the citationSources property to citations in CitationMetadata . The CitationSource type was renamed to Citation to match other platforms.

    • Unnecessary public types, methods, and properties

      • Removed the following types, methods, and properties which were unintentionally exposed: defaultTimeout , CountTokensResponseFields , parseCountTokensResponse , parseEmbedContentResponse , parseGenerateContentResponse , parseContent , BatchEmbedContentsResponse , ContentEmbedding , EmbedContentRequest , and EmbedContentResponse .

    • Count tokens

      • Removed extra fields from the countTokens function that are no longer necessary. Only contents is needed.

    • Instantiating a model

      • Moved the systemInstruction parameter to the end of the parameter list to align with other platforms.

    • Embedding functionality

      • Removed unsupported embedding functionality ( embedContent and batchEmbedContents ) from the model.

    Unity

    Support for Unity wasn't available from Google AI client SDKs.

    Learn how to get started with the Firebase AI Logic  SDK for Unity .


    Give feedback about your experience with Firebase AI Logic


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