Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.

Firebase AI Logic Basics Overview Firebase AI Logic is a product of Firebase that allows developers to add gen AI to their mobile and web apps using client side SDKs. You can call Gemini models directly from your app without managing a dedicated backend. Firebase AI Logic, which was previously known as "Vertex AI for Firebase", represents the evolution of Google's AI integration platform for mobile and web developers. It supports the two Gemini API providers: Gemini Developer API : It has a free tier ideal for prototyping, and pay as you go for production Agent Platform Gemini API (formerly branded Vertex AI): Ideal for scale with enterprise grade production readiness, requires Blaze plan Use the Gemini Developer API as a default, and only Agent Platform Gemini API (formerly branded Vertex AI) if the application requires it. Setup & Initialization Prerequisites Before starting, ensure you have Node.js 16+ and npm installed. Install them if they aren’t already available. Identify the platform the user is interested in building on prior to starting: Android, iOS, Flutter or Web. If their platform is unsupported, Direct the user to Firebase Docs to learn how to set up AI Logic for their application (share this link with the user https://firebase.google.com/docs/ai logic/get started) Installation The library is part of the standard Firebase Web SDK. npm install g firebase@latest If you're in a firebase directory (with a firebase.json) the currently selected project will be marked with "current" using this command: npx y firebase tools@latest projects:list Ensure there's at least one app associated with the current project npx y firebase tools@latest apps:list Initialize AI logic SDK with the init command npx y firebase tools@latest init ailogic This will automatically enable the Gemini Developer API in the Firebase console. More info in [Firebase AI Logic Getting Started](https://firebase.google.com/docs/ai logic/get started.md.txt) Core Capabilities [!WARNING] CRITICAL: Use current model names: Always check the [Firebase AI Logic Models documentation](https://firebase.google.com/docs/ai logic/models.md.txt) for the currently supported model names. Do NOT use gemini 2.0 pro or gemini 2.0 flash or other older models that are shutdown. Text Only Generation Multimodal (Text + Images/Audio/Video/PDF input) Firebase AI Logic allows Gemini models to analyze image files directly from your app. This enables features like creating captions, answering questions about images, detecting objects, and categorizing images. Beyond images, Gemini can analyze other media types like audio, video, and PDFs by passing them as inline data with their MIME type. For files larger than 20 megabytes (which can cause HTTP 413 errors as inline data), store them in Cloud Storage for Firebase and pass their URLs to the Gemini Developer API. Chat Session (Multi turn) Maintain history automatically using startChat . Streaming Responses To improve the user experience by showing partial results as they arrive (like a typing effect), use generateContentStream instead of generateContent for faster display of results. Generate Images with Nano Banana [!WARNING] Use current Image model names: Always check the [Firebase AI Logic Models documentation](https://firebase.google.com/docs/ai logic/models.md.txt) for the currently supported image generation (Nano Banana) model names. Requires an upgraded Blaze pay as you go billing plan. Search Grounding with the built in googleSearch tool Supported Platforms and Frameworks Supported Platforms and Frameworks include Kotlin and Java for Android, Swift for iOS, JavaScript for web apps, Dart for Flutter, and C Sharp for Unity. Advanced Features Structured Output (JSON) Enforce a specific JSON schema for the response. On Device AI (Hybrid) Hybrid on device inference for web apps, where the Firebase Javascript SDK automatically checks for Gemini Nano's availability (after installation) and switches between on device or cloud hosted prompt execution. This requires specific steps to enable model usage in the Chrome browser, more info in the [hybrid on device inference documentation](https://firebase.google.com/docs/ai logic/hybrid on device inference.md.txt). Security & Production App Check [!WARNING] Critical Safety Requirement: In order to use AI Logic safely, you MUST set up App Check on your app. This prevents unauthorized clients from using your API quota and accessing your backend resources. See [App Check with reCAPTCHA Enterprise](https://firebase.google.com/docs/app check/web/recaptcha enterprise provider.md.txt) for setup instructions. App Check Debug Tokens for Local Development & CI/CD Because App Check attestation providers (like Play Integrity or DeviceCheck) reject emulators, simulators, or CI environments, you must use App Check Debug Tokens during development and testing to bypass standard attestation. Local Development (Auto Generated) 1. Configure your code's App Check provider to use the debug factory: Web : Set self.FIREBASE APPCHECK DEBUG TOKEN = true; before initializing App Check. Android : Install DebugAppCheckProviderFactory.getInstance() . iOS : Set provider factory to AppCheckDebugProviderFactory() . 2. Run your app in the emulator/localhost. 3. Look at your runtime debugger console / Logcat logs for the generated UUID: Example: AppCheck debug token: "123a4567 b89c 12d3 e456 789012345678" 4. Register this token in the Firebase Console under Security App Check Apps Manage debug tokens . CI/CD Pipelines (Pre Provisioned) 1. Generate and register a new debug token in the Firebase Console under Security App Check Apps Manage debug tokens . 2. Add this token string as an encrypted secret in your CI system (e.g. APP CHECK DEBUG TOKEN ). 3. Configure your build to pass this secret as an environment variable to the SDK during test execution (e.g. self.FIREBASE APPCHECK DEBUG TOKEN = process.env.APP CHECK DEBUG TOKEN ). Remote Config Consider that you do not need to hardcode model names (e.g., a specific model version string). Use Firebase Remote Config to update model versions dynamically without deploying new client code. See [Changing model names remotely](https://firebase.google.com/docs/ai logic/change model name remotely.md.txt) [!WARNING] CRITICAL: Backend Provisioning Required For all platforms (Flutter, Android, iOS, Web), you MUST run npx firebase tools init ailogic to provision the service. flutterfire configure ONLY handles client configuration and does NOT enable the AI service, leading to PERMISSION DENIED errors. Initialization Code References Language, Gemini API Context URL : Framework, : provider : : : Platform : : : : : : Web Modular Gemini firebase://docs/ai logic/get started : API : Developer : : : : API : : : : (Developer : : : : API) : : iOS (Swift) Gemini [ios setup.md](references/ios setup.md) : : Developer : : : : API : : Flutter Gemini [flutter setup.md](references/flutter setup.md) : (Dart) : Developer : : : : API : : [!WARNING] CRITICAL: Use current model names: Always check the [Firebase AI Logic Models documentation](https://firebase.google.com/docs/ai logic/models.md.txt) for the currently supported model names. Do NOT use gemini 2.0 pro or gemini 2.0 flash or other older models that are shutdown. References [Web SDK code examples and usage patterns](references/usage patterns web.md) [iOS SDK code examples and usage patterns](references/ios setup.md) [Flutter SDK code examples and usage patterns](references/flutter setup.md) [Android (Kotlin) SDK usage patterns](references/usage patterns android.md)

firebase-ai-logic-basics