firebase-ai-logic-basics

# 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 [!NOTE] `firebase_vertexai` has been replaced by `firebase_ai`.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`:

```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.

```dart
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](https://firebase.google.com/docs/ai-logic/models.md.txt)
> 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 Agent Platform Gemini API (formerly branded
> Vertex AI) unless the app and business use case specifically require
> enterprise-grade scalability or data residency requirements. Note that the
> Gemini Developer API *usually does not* require the Firebase project to be on
> the pay-as-you-go Blaze pricing plan; however, the Agent Platform Gemini API
> does require the Blaze plan.

### Text Generation

```dart
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

```dart
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.

```swift
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.

```swift
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.

```swift
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.

```swift
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.*

```swift
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:

```bash
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:

```kotlin
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:

```kotlin
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`:

```kotlin
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:

```kotlin
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:

```kotlin
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:

```kotlin
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.

```JavaScript
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

```JavaScript
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.

```JavaScript
// 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.

```JavaScript
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).

```JavaScript
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

```Javascript
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.

```JavaScript
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.

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

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

---
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 -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)