firebase-ai-logic-basics

• 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)

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

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.

Maintain history automatically using startChat.

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.

[!WARNING] Use current Image model names: Always check the Firebase AI Logic Models documentation for the currently supported image generation (Nano Banana) model names.

• Requires an upgraded Blaze pay-as-you-go billing plan.

Enforce a specific JSON schema for the response.

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.

[!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 for setup instructions.

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.

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.

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).

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

[!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.