Kai Engine Firebase AI

Pub Version GitHub

A Firebase AI adapter for the Kai Engine.

Overview

This package provides Firebase AI (Gemini) integration for the Kai Engine, allowing you to easily use Firebase's generative AI services with the Kai Engine's pipeline architecture.

Features

  • Seamless integration with Firebase AI (Gemini models)
  • Implements GenerationServiceBase interface from Kai Engine
  • Streaming responses with real-time text updates
  • Tool/function calling support with automatic execution loop
  • Token counting support
  • Configurable safety settings and generation parameters
  • Built-in infinite loop detection for tool calls

Getting Started

Add the dependencies to your pubspec.yaml:

dependencies:
  kai_engine: ^0.1.1
  kai_engine_firebase_ai: ^0.1.1
  firebase_core: ^3.0.0
  firebase_ai: ^3.2.0

Or install via command line:

dart pub add kai_engine kai_engine_firebase_ai firebase_core firebase_ai

Setup

  1. Set up Firebase in your project following the Firebase setup guide.

  2. Enable the Vertex AI for Firebase API in your Firebase project.

Usage

Basic Setup

import 'package:firebase_core/firebase_core.dart';
import 'package:firebase_ai/firebase_ai.dart';
import 'package:kai_engine/kai_engine.dart';
import 'package:kai_engine_firebase_ai/kai_engine_firebase_ai.dart';

// Initialize Firebase
await Firebase.initializeApp();

// Create the Firebase AI instance
final firebaseAi = FirebaseAI.googleAI();

// Create the generation service
final generationService = FirebaseAiGenerationService(
  firebaseAi: firebaseAi,
  config: GenerativeConfig(
    model: 'gemini-2.0-flash',
    generationConfig: GenerationConfig(
      temperature: 0.7,
      maxOutputTokens: 2048,
    ),
  ),
);

// Use with your chat controller
final chatController = MyChatController(
  conversationManager: conversationManager,
  generationService: generationService,
);

With Tool/Function Calling

// Define a tool call model
class WeatherCall {
  final String location;
  WeatherCall({required this.location});

  factory WeatherCall.fromJson(Map<String, Object?> json) {
    return WeatherCall(location: json['location'] as String);
  }
}

// Define a tool schema
final class WeatherToolSchema extends FirebaseAiToolSchema<WeatherCall, String> {
  WeatherToolSchema()
    : super(
        parser: WeatherCall.fromJson,
        declaration: FunctionDeclaration(
          'get_weather',
          'Get the current weather for a location',
          parameters: {
            'location': Schema.string(description: 'City name'),
          },
        ),
      );

  @override
  Future<ToolResult<String>> execute(WeatherCall call) async {
    // Fetch weather data...
    final weather = 'Sunny, 25°C in ${call.location}';
    return ToolResult.success(weather, {'weather': weather});
  }
}

// Create service with tools
final generationService = FirebaseAiGenerationService(
  firebaseAi: firebaseAi,
  config: GenerativeConfig(
    model: 'gemini-2.0-flash',
    toolSchemas: [WeatherToolSchema()],
  ),
);

Streaming Responses

// The service automatically handles streaming
controller.generationStateStream.listen((state) {
  state.when(
    initial: () {},
    loading: (_) => print('Starting...'),
    streamingText: (text) => print('Received: $text'),
    functionCalling: (name) => print('Calling function: $name'),
    complete: (result) => print('Complete!'),
    error: (e) => print('Error: $e'),
  );
});

await controller.submit('What is the weather in Tokyo?');

Custom Message Adapter

// Use custom adapter if needed
final generationService = FirebaseAiGenerationService(
  firebaseAi: firebaseAi,
  config: config,
  messageAdapter: MyCustomContentAdapter(),
);

Configuration Options

GenerativeConfig

Parameter Type Description
model String The Gemini model to use (e.g., 'gemini-2.0-flash')
safetySettings List<SafetySetting>? Safety settings for content filtering
generationConfig GenerationConfig? Generation parameters (temperature, tokens, etc.)
toolSchemas List<FirebaseAiToolSchema>? Tool definitions for function calling
toolConfig ToolingConfig? Tool execution configuration
systemPrompt String? Default system prompt (overridden by ContextEngine)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Libraries

kai_engine_firebase_ai