/firebase-ai-logic-basics

name: firebase-ai-logic-basics
description: Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.
version: 1.0.1
metadata:
  category: AiAndMachineLearning

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 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 tok