Firebase Ai Logic Basics

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

• Vertex AI Gemini API: Ideal for scale with enterprise-grade production

readiness, requires Blaze plan

Use the Gemini Developer API as a default, and only Vertex AI Gemini API 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

Core Capabilities

[!WARNING] CRITICAL: Use current model names: Always check the

Firebase AI Logic Models documentation

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

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.

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 for setup instructions.

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

[!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

[!WARNING] CRITICAL: Use current model names: Always check the

Firebase AI Logic Models documentation

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 iOS SDK code examples and usage patterns Flutter SDK code examples and usage patterns

Android (Kotlin) SDK usage patterns

Flutter Setup for Firebase AI Logic

This guide covers how to integrate Firebase AI Logic (Gemini API) into your Flutter applications.

[!IMPORTANT] Foundational Workflows & CLI-First Approach:

>

1. Review Foundation: Before implementing platform-specific code, ALWAYS

review the foundational firebase-basics skill to ensure familiarity with

core workflows.

1. Backend Provisioning via CLI: Use the Firebase CLI for backend setup.

Running npx firebase-tools init ailogic is MANDATORY to provision the

service. flutterfire configure does NOT enable the AI service and will

result in PERMISSION_DENIED if skipped.

1. Client Configuration: Use flutterfire configure strictly for

generating firebase_options.dart. Avoid manual Console configuration.

[!NOTE] firebase_vertexai has been replaced by firebase_ai. Always use

firebase_ai for new projects.

Installation

Add the necessary Firebase dependencies to your pubspec.yaml:

dependencies:
  flutter:
    sdk: flutter
  firebase_core: ^4.0.0
  firebase_auth: ^6.0.0
  firebase_ai: ^3.0.0

Run flutter pub get to install the packages.

Initialization

Initialize Firebase and sign in (anonymously or via authenticated user) before using AI Logic.

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

void main() async {
  WidgetsFlutterBinding.ensureInitialized();
  await Firebase.initializeApp();
  await FirebaseAuth.instance.signInAnonymously();
  runApp(const MyApp());
}

Usage

Use FirebaseAI.googleAI for the Gemini Developer API.

[!IMPORTANT] Model Selection: Refer to

Firebase AI Logic Models

to find the latest supported model. Do not use old models like

gemini-2.0-pro or gemini-2.0-flash.

[!IMPORTANT] Choose the Right API Provider: Always use

FirebaseAI.googleAI (Gemini Developer API) as the default for prototyping

and standard use. Avoid using the Vertex AI Gemini API unless your application

specifically requires enterprise-grade scalability and is configured for the

Blaze plan.

Text Generation

import 'package:firebase_ai/firebase_ai.dart';
import 'package:firebase_auth/firebase_auth.dart';

Future<String> generateText(String prompt) async {
  final googleAI = FirebaseAI.googleAI(auth: FirebaseAuth.instance);
  
  // [AGENT] Replace '<latest_supported_model>' with the latest model from https://firebase.google.com/docs/ai-logic/models.md.txt
  final model = googleAI.generativeModel(model: '<latest_supported_model>');

  final response = await model.generateContent([Content.text(prompt)]);
  return response.text ?? 'No response';
}

Chat Session

final chat = model.startChat(history: [
  Content.text('Hello, I am a user.'),
  Content.model([TextPart('Hello! How can I help you today?')]),
]);

final response = await chat.sendMessage(Content.text('What is CBT?'));

Firebase AI Logic iOS Setup Guide

1. Import and Initialize

Ensure you have installed the FirebaseAILogic SDK via Swift Package Manager.

import FirebaseAILogic

// Initialize the Firebase AI service and the generative model.
let ai = FirebaseAI.firebaseAI()

// [AGENT] Replace "<latest_supported_model>" with the latest model from https://firebase.google.com/docs/ai-logic/models.md.txt
let model = ai.generativeModel(modelName: "<latest_supported_model>")

2. SwiftUI Integration (Best Practices)

Use the @Observable pattern to manage AI state and provide a smooth UX with loading indicators and error handling.

⛔️ CRITICAL WARNING: Do NOT initialize the model inline as a class

property if there's any chance the view model is instantiated before

FirebaseApp.configure() executes in the app root. To be safe, initialize the

model lazily or pass it in from a point in the hierarchy where Firebase is

guaranteed to be configured.

import SwiftUI
import FirebaseAILogic

@MainActor
@Observable
final class AIViewModel {
    // [AGENT] Replace with the latest model from https://firebase.google.com/docs/ai-logic/models.md.txt
    private lazy var model = FirebaseAI.firebaseAI().generativeModel(modelName: "<latest_supported_model>")
    
    var responseText: String = ""
    var isFetching: Bool = false
    var errorMessage: String?
    
    func generate(prompt: String) async {
        isFetching = true
        errorMessage = nil
        defer { isFetching = false }
        
        do {
            let response = try await model.generateContent(prompt)
            self.responseText = response.text ?? "No response"
        } catch {
            self.errorMessage = error.localizedDescription
        }
    }
}

struct AIView: View {
    @State private var viewModel = AIViewModel()
    @State private var prompt = "Write a story about a magic backpack."
    
    var body: some View {
        VStack {
            TextField("Enter prompt", text: $prompt)
            
            Button("Generate") {
                Task { await viewModel.generate(prompt: prompt) }
            }
            .disabled(viewModel.isFetching)
            
            if viewModel.isFetching {
                ProgressView()
            } else if let error = viewModel.errorMessage {
                Text(error).foregroundStyle(.red)
            } else {
                ScrollView {
                    Text(viewModel.responseText)
                }
            }
        }
        .padding()
    }
}

3. Safety Settings

You can configure safety thresholds to prevent the model from generating harmful content.

let safetySettings = [
  SafetySetting(category: .harassment, threshold: .blockLowAndAbove),
  SafetySetting(category: .hateSpeech, threshold: .blockMediumAndAbove)
]

let model = FirebaseAI.firebaseAI().generativeModel(
  modelName: "<latest_supported_model>", // [AGENT] Replace with the latest model from https://firebase.google.com/docs/ai-logic/models.md.txt
  safetySettings: safetySettings
)

Advanced Features

Chat Session (Multi-turn)

Chat sessions persist state across multiple interactions, which is essential for ongoing conversations or when using tools like function calling.

let chat = model.startChat()

Task {
    do {
        let response1 = try await chat.sendMessage("Hello! I have two dogs in my house.")
        print(response1.text ?? "")

        let response2 = try await chat.sendMessage("How many paws are in my house?")
        print(response2.text ?? "")
    } catch {
        print("Error in chat: \(error)")
    }
}

Function Calling (Tools)

Define functions that the model can request to execute to interact with external systems. Note: Advanced workflows like function calling generally require a multi-turn Chat Session to handle the back-and-forth execution.

let getStockPriceTool = Tool(functionDeclarations: [
  FunctionDeclaration(
    name: "getStockPrice",
    description: "Get the current stock price for a given symbol.",
    parameters: [
      "symbol": Schema(
        type: .string,
        description: "The stock symbol, e.g. AAPL"
      )
    ]
  )
])

let model = FirebaseAI.firebaseAI().generativeModel(
  modelName: "<latest_supported_model>", // [AGENT] Replace with the latest model from https://firebase.google.com/docs/ai-logic/models.md.txt
  tools: [getStockPriceTool]
)

// In your task (using a chat session):
let chat = model.startChat()
let response = try await chat.sendMessage("What is the stock price of Apple?")
if let functionCall = response.functionCalls.first {
    // Handle the function call (e.g. call a local API and send the result back)
    print("Model requested function: \(functionCall.name) with args: \(functionCall.args)")
}

Firebase AI Logic on Android (Kotlin)

First, ensure you have initialized the Firebase App (see firebase-basics skill). Then, initialize the AI Logic service as below

0. Enable Firebase AI Logic via CLI

Before adding dependencies in your app, make sure you enable the AI Logic service in your Firebase Project using the Firebase CLI:

npx -y firebase-tools@latest init
# When prompted, select 'AI logic' to enable the Gemini API in your project.

______________________________________________________________________

1. Add Dependencies

In your module-level build.gradle.kts (usually app/build.gradle.kts), add the dependency for Firebase AI:

dependencies {
    // [AGENT] Fetch the latest available BoM version from https://firebase.google.com/support/release-notes/android before adding this
    implementation(platform("com.google.firebase:firebase-bom:<latest_bom_version>"))

    // Add the dependency for the Firebase AI library
    implementation("com.google.firebase:firebase-ai")
}

______________________________________________________________________

2. Initialize and Generate Content

In your Activity or Fragment, initialize the FirebaseAI service and generate content using a Gemini model:

import com.google.firebase.ai.FirebaseAI
import com.google.firebase.ai.ktx.ai
import com.google.firebase.ktx.Firebase

class MainActivity : AppCompatActivity() {

    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        setContentView(R.layout.activity_main)

        // Initialize Firebase AI
        val ai = Firebase.ai

        // [AGENT] Replace "<latest_supported_model>" with the latest model from https://firebase.google.com/docs/ai-logic/models.md.txt
        val model = ai.generativeModel("<latest_supported_model>")

        // Generate content
        lifecycleScope.launch {
            try {
                val response = model.generateContent("Write a story about a magic backpack.")
                Log.d(TAG, "Response: ${response.text}")
            } catch (e: Exception) {
                Log.e(TAG, "Error generating content", e)
            }
        }
    }
}

Jetpack Compose (Modern)

Initialize inside a ComponentActivity and use setContent:

import android.os.Bundle
import androidx.activity.ComponentActivity
import androidx.activity.compose.setContent
import androidx.compose.material3.MaterialTheme
import androidx.compose.material3.Text
import androidx.lifecycle.lifecycleScope
import com.google.firebase.Firebase
import com.google.firebase.ai.ai
import kotlinx.coroutines.launch

class MainActivity : ComponentActivity() {
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        val ai = Firebase.ai
        // [AGENT] Replace with the latest model from https://firebase.google.com/docs/ai-logic/models.md.txt
        val model = ai.generativeModel("<latest_supported_model>")
        
        lifecycleScope.launch {
            val response = model.generateContent("Hello Gemini!")
            setContent {
                MaterialTheme {
                    Text("AI Response: ${response.text}")
                }
            }
        }
    }
}

______________________________________________________________________

3. Multimodal Input (Text and Images)

Pass bitmap data along with text prompts:

val image1: Bitmap = ... // Load your bitmap
val image2: Bitmap = ...

val response = model.generateContent(
    content("Analyze these images for me") {
        image(image1)
        image(image2)
        text("Compare these two items.")
    }
)
Log.d(TAG, response.text)

______________________________________________________________________

4. Chat Session (Multi-turn)

Maintain chat history automatically:

val chat = model.startChat(
    history = listOf(
        content("user") { text("Hello, I am a software engineer.") },
        content("model") { text("Hello! How can I help you today?") }
    )
)

lifecycleScope.launch {
    val response = chat.sendMessage("What should I learn next?")
    Log.d(TAG, response.text)
}

______________________________________________________________________

5. Streaming Responses

For faster display, stream the response:

lifecycleScope.launch {
    model.generateContentStream("Tell me a long story.")
        .collect { chunk ->
            print(chunk.text) // Update UI incrementally
        }
}

Firebase AI Logic Basics

Initialization Pattern

You must initialize the ai-logic service after the main Firebase App.

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";


// If running in Firebase App Hosting, you can skip Firebase Config and instead use:
// const app = initializeApp();

const firebaseConfig = {
  // ... your firebase config
};

const app = initializeApp(firebaseConfig);

// Initialize the AI Logic service (defaults to Gemini Developer API)
// To set the AI provider, set the backend as the second parameter
const ai = getAI(app, { backend: new GoogleAIBackend() });

const generationConfig = {
  candidate_count: 1,
  maxOutputTokens: 2048,
  stopSequences: [],
  temperature: 0.7,      // Balanced: creative but focused
  topP: 0.95,            // Standard: allows a wide range of probable tokens
  topK: 40,              // Standard: considers the top 40 tokens
};

// Specify the config as part of creating the `GenerativeModel` instance
// [AGENT] Replace "<latest_supported_model>" with the latest model from https://firebase.google.com/docs/ai-logic/models.md.txt
const model = getGenerativeModel(ai, { model: "<latest_supported_model>",  generationConfig });

Core Capabilities

Text-Only Generation

async function generateText(prompt) {
  const result = await model.generateContent(prompt);
  const response = await result.response;
  return response.text();
}

Multimodal (Text + Images/Audio/Video/PDF input)

Firebase AI Logic accepts Base64 encoded data or specific file references.

// Helper to convert file to base64 generic object
async function fileToGenerativePart(file) {
  const base64EncodedDataPromise = new Promise((resolve) => {
    const reader = new FileReader();
    reader.onloadend = () => resolve(reader.result.split(',')[1]);
    reader.readAsDataURL(file);
  });
  
  return {
    inlineData: {
      data: await base64EncodedDataPromise,
      mimeType: file.type,
    },
  };
}

async function analyzeImage(prompt, imageFile) {
  const imagePart = await fileToGenerativePart(imageFile);
  const result = await model.generateContent([prompt, imagePart]);
  return result.response.text();
}

Chat Session (Multi-turn)

Maintain history automatically using startChat.

const chat = model.startChat({
  history: [
    {
      role: "user",
      parts: [{ text: "Hello, I am a developer." }],
    },
    {
      role: "model",
      parts: [{ text: "Great to meet you. How can I help with code?" }],
    },
  ],
});

async function sendMessage(msg) {
  const result = await chat.sendMessage(msg);
  return result.response.text();
}

Streaming Responses

For real-time UI updates (like a typing effect).

async function streamResponse(prompt) {
  const result = await model.generateContentStream(prompt);
  for await (const chunk of result.stream) {
    const chunkText = chunk.text();
    console.log("Stream chunk:", chunkText);
    // Update UI here
  }
}

Generate Images with Nano Banana

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend, ResponseModality } from "firebase/ai";


// 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: "<latest_supported_image_model>", // [AGENT] Replace with the latest image model from https://firebase.google.com/docs/ai-logic/models.md.txt
  // Configure the model to respond with text and images (required)
  generationConfig: {
    responseModalities: [ResponseModality.TEXT, ResponseModality.IMAGE],
  },
});

// Provide a text prompt instructing the model to generate an image
const prompt = 'Generate an image of the Eiffel Tower with fireworks in the background.';

// To generate an image, call `generateContent` with the text input
const result = model.generateContent(prompt);

// Handle the generated image
try {
  const inlineDataParts = result.response.inlineDataParts();
  if (inlineDataParts?.[0]) {
    const image = inlineDataParts[0].inlineData;
    console.log(image.mimeType, image.data);
  }
} catch (err) {
  console.error('Prompt or candidate was blocked:', err);
}

Advanced Features

Structured Output (JSON) Enforce a specific JSON schema for the response.

import { getGenerativeModel, Schema } from "firebase/ai";
const jsonModel = getGenerativeModel(ai, {
    model: "<latest_supported_model>", // [AGENT] Replace with the latest model from https://firebase.google.com/docs/ai-logic/models.md.txt
    generationConfig: {
        responseMimeType: "application/json",
        // Optional: Define a schema
        schema = Schema.object({ ... });
    }
});

async function getJsonData(prompt) {
    const result = await jsonModel.generateContent(prompt);
    return JSON.parse(result.response.text());
}

On-Device AI (Hybrid) Automatically switch between local Gemini Nano and cloud models based on device capability.

import {getGenerativeModel, InferenceMode } from "firebase/ai";

const hybridModel = getGenerativeModel(ai, { mode: InferenceMode.PREFER_ON_DEVICE });