dartantic_firebase_ai 0.2.0

Firebase AI provider for dartantic_ai. Provides access to Google's Gemini models through Firebase with App Check security and Firebase Auth integration.

dartantic_firebase_ai

Firebase AI provider for dartantic_ai.

Provides access to Google's Gemini models through Firebase with flexible backend options for both development and production use.

Features

• 🔥 Dual Backend Support - Google AI (development) and Vertex AI (production)
• 🔒 Enhanced Security - App Check and Firebase Auth support (Vertex AI)
• 🎯 Most Gemini Capabilities - Chat, media generation, structured output, vision
• 🚀 Streaming Responses - Real-time token generation
• 🛠️ Tool Calling - Function execution during generation
• 🧠 Extended Thinking - Model reasoning with configurable token budgets
• 🔄 Easy Migration - Switch backends without code changes

Platform Support

• ✅ iOS
• ✅ Android
• ✅ macOS
• ✅ Web

Note: This is a Flutter-specific package and requires the Flutter SDK.

Installation

Add to your pubspec.yaml:

dependencies:
  dartantic_firebase_ai: ^0.1.0
  firebase_core: ^4.4.0

To use with the Agent orchestration layer, also add dartantic_ai:

dependencies:
  dartantic_ai: ^3.0.0
  dartantic_firebase_ai: ^0.1.0
  firebase_core: ^4.4.0

Setup Requirements

Important: Both backends require Flutter SDK, Firebase Core initialization, and a Firebase project configuration.

Common Requirements (Both Backends)

• Flutter SDK (not just Dart)
• Firebase Core initialization (Firebase.initializeApp())
• Firebase project configuration (minimal config acceptable)

Google AI Backend (Development)

• Uses Gemini Developer API through Firebase SDK
• Requires Google AI API key for authentication
• Simpler authentication setup
• Good for prototyping and development

Vertex AI Backend (Production)

• Uses Vertex AI through Firebase infrastructure
• Requires full Firebase project setup with Google Cloud billing enabled
• Follow the Firebase Flutter setup guide for your platform
• Enable Firebase AI Logic in your Firebase console
• (Optional) Set up App Check for enhanced security

Usage

Backend Selection

Firebase AI supports two backends with different API endpoints but similar setup:

Google AI Backend (for development/testing):

• Routes requests to Gemini Developer API
• Good for prototyping and development

Vertex AI Backend (for production):

• Requires complete Firebase project setup
• Full Firebase integration with security features
• App Check, Firebase Auth support
• Production-ready infrastructure

Basic Setup

import 'package:dartantic_firebase_ai/dartantic_firebase_ai.dart';
import 'package:dartantic_interface/dartantic_interface.dart';
import 'package:firebase_core/firebase_core.dart';

// Initialize Firebase (required for both backends)
await Firebase.initializeApp();

// Create a provider
final provider = FirebaseAIProvider(backend: FirebaseAIBackend.googleAI);

// Create a chat model
final chatModel = provider.createChatModel(name: 'gemini-2.5-flash');

// Stream a response
final messages = [ChatMessage.user('Explain quantum computing')];
await for (final chunk in chatModel.sendStream(messages)) {
  print(chunk.output.text);
}

With Agent (requires dartantic_ai)

import 'package:dartantic_ai/dartantic_ai.dart';
import 'package:dartantic_firebase_ai/dartantic_firebase_ai.dart';

// Register provider factories
Agent.providerFactories['firebase-vertex'] = () =>
    FirebaseAIProvider(backend: FirebaseAIBackend.vertexAI);
Agent.providerFactories['firebase-google'] = () =>
    FirebaseAIProvider(backend: FirebaseAIBackend.googleAI);

// Create agents
final prodAgent = Agent('firebase-vertex:gemini-2.0-flash');
final devAgent = Agent('firebase-google:gemini-2.0-flash');

// Send a message
final result = await prodAgent.send('Explain quantum computing');
print(result.output);

// Stream a response
await for (final chunk in devAgent.sendStream('Tell me a story')) {
  print(chunk.output);
}

Configuration Options

The FirebaseAIChatModelOptions class supports:

• temperature - Sampling temperature (0.0 to 2.0)
• topP - Nucleus sampling threshold
• topK - Top-K sampling
• candidateCount - Number of generated responses to return
• maxOutputTokens - Maximum tokens to generate
• stopSequences - Stop generation sequences
• responseMimeType - Output response MIME type (e.g., application/json)
• responseSchema - Output response schema for structured output
• safetySettings - Content safety configuration
• enableCodeExecution - Enable code execution in the model
• enableThinking - Enable model reasoning/thinking content
• thinkingBudgetTokens - Token budget for thinking (-1 for dynamic)

Security Best Practices

1. Use App Check to prevent unauthorized API usage
2. Enable Firebase Auth for user-based access control
3. Set up Firebase Security Rules to protect your data
4. Monitor usage in Firebase console to detect anomalies

Dependencies and Requirements

This package requires Flutter - it cannot be used in pure Dart projects due to:

• Flutter-specific Firebase SDK dependencies (firebase_core, firebase_auth, etc.)
• Platform-specific Firebase initialization code
• Flutter framework dependencies for UI integrations

For pure Dart projects, consider using the dartantic_google provider instead.

Comparison to Google Provider

Feature	Google Provider	Firebase AI Provider
API Access	Direct Gemini API	Through Firebase
Setup	API key only	Firebase project + API key
Security	API key only	App Check + Auth
Platforms	All Dart platforms	Flutter only
Embeddings	Yes	No
Media Generation	Yes	Yes
On-Device	No	No (web only)
Cost Control	Manual	Firebase quotas
Dependencies	HTTP client only	Full Firebase SDK

Note: On-device inference is available for web apps via Firebase AI Logic, but not yet supported for Flutter mobile apps.

Contributing

Contributions welcome! See the contributing guide.

License

MIT License - see LICENSE