Masamune AI Firebase

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Masamune AI Firebase

Overview

• masamune_ai_firebase provides Firebase Vertex AI / Gemini integration for the Masamune AI framework.
• Extends masamune_ai with FirebaseAIMasamuneAdapter for Firebase-powered generative AI.
• Supports Gemini 2.0 Flash and other Firebase AI models.
• Includes automatic function-calling support via Vertex AI when using MCP tools.
• Optionally enables Firebase App Check for secure API access.

Setup

1. Add the package to your project.

flutter pub add masamune_ai
flutter pub add masamune_ai_firebase

2. Initialize Firebase and register FirebaseAIMasamuneAdapter in MasamuneApp.

import "package:masamune_ai_firebase/masamune_ai_firebase.dart";

void main() {
  runApp(
    MasamuneApp(
      appRef: appRef,
      adapters: const [
        FirebaseAIMasamuneAdapter(
          options: DefaultFirebaseOptions.currentPlatform,  // Firebase config
          model: FirebaseAIModel.gemini20Flash,             // AI model
          enableAppCheck: true,                              // Optional: Enable App Check
          defaultConfig: AIConfig(
            systemPromptContent: AIContent.system([
              AIContent.model(text: "You are a helpful assistant."),
            ]),
          ),
        ),
      ],
    ),
  );
}

FirebaseAIMasamuneAdapter Configuration

Required Parameters

• options (FirebaseOptions): Firebase project configuration from firebase_options.dart
• model (FirebaseAIModel): The Gemini model to use (see Available Models section)

Optional Parameters

• defaultConfig (AIConfig): Default system prompt and response schema
• enableAppCheck (bool): Enable Firebase App Check for API security (default: false)
• contentFilter: Preprocess messages before sending to the AI
• onGenerateFunctionCallingConfig: Control function-calling retry logic
• mcpServerConfig, mcpClientConfig, mcpFunctions: MCP tool integration settings

Available Models

The FirebaseAIModel enum provides the following Gemini models:

FirebaseAIModel.gemini20Flash         // Gemini 2.0 Flash (default)
FirebaseAIModel.gemini15Flash         // Gemini 1.5 Flash
FirebaseAIModel.gemini15FlashLatest   // Gemini 1.5 Flash (latest version)
FirebaseAIModel.gemini15Flash8B       // Gemini 1.5 Flash 8B (lightweight)
FirebaseAIModel.gemini15Pro           // Gemini 1.5 Pro
FirebaseAIModel.gemini15ProLatest     // Gemini 1.5 Pro (latest version)

Key Features

Automatic Initialization

The adapter automatically initializes Firebase and caches GenerativeModel instances per AIConfigKey (config + tools combination). Call initialize() once before generating content:

final adapter = FirebaseAIMasamuneAdapter.primary;
await adapter.initialize();

Function Calling Support

When MCP tools are provided via AIThread or AISingle, they are automatically converted to Vertex AI function declarations:

final thread = ref.app.controller(
  AIThread.query(
    threadId: "chat-1",
    tools: {weatherTool},  // Automatically converted to Vertex AI functions
  ),
);

Control function-calling behavior with onGenerateFunctionCallingConfig:

FirebaseAIMasamuneAdapter(
  options: DefaultFirebaseOptions.currentPlatform,
  model: FirebaseAIModel.gemini20Flash,
  onGenerateFunctionCallingConfig: (attempt) => AIGenerateFunctionCallingConfig(
    maxRetries: 3,
    forceExecution: attempt > 0,
  ),
)

App Check Integration

Enable Firebase App Check for additional API security:

FirebaseAIMasamuneAdapter(
  options: DefaultFirebaseOptions.currentPlatform,
  model: FirebaseAIModel.gemini20Flash,
  enableAppCheck: true,  // Requires Firebase App Check setup
)

Usage with AIThread and AISingle

For conversation management and content generation, use AIThread (multi-turn) and AISingle (single-turn) controllers from the masamune_ai package:

// Multi-turn conversation
final thread = ref.app.controller(
  AIThread.query(
    threadId: "chat-1",
    initialContents: [AIContent.text("Hello!")],
  ),
);

await thread.generateContent([
  AIContent.text("Tell me about Firebase Gemini API"),
]);

// Single-turn interaction
final single = ref.app.controller(AISingle.query());
final result = await single.generateContent([
  AIContent.text("Summarize this"),
]);

Complete Example

import "package:masamune_ai/masamune_ai.dart";
import "package:masamune_ai_firebase/masamune_ai_firebase.dart";

void main() {
  runApp(
    MasamuneApp(
      appRef: appRef,
      adapters: const [
        FirebaseAIMasamuneAdapter(
          options: DefaultFirebaseOptions.currentPlatform,
          model: FirebaseAIModel.gemini20Flash,
          defaultConfig: AIConfig(
            systemPromptContent: AIContent.system([
              AIContent.model(text: "You are a helpful AI assistant."),
            ]),
          ),
        ),
      ],
      child: MyApp(),
    ),
  );
}

class MyApp extends PageScopedWidget {
  @override
  Widget build(BuildContext context, PageRef ref) {
    final thread = ref.app.controller(
      AIThread.query(threadId: "main-chat"),
    );

    return Scaffold(
      body: ListView.builder(
        itemCount: thread.value.length,
        itemBuilder: (context, index) {
          final content = thread.value[index];
          return ListTile(
            title: Text(content.role.name),
            subtitle: Text(content.text),
          );
        },
      ),
      floatingActionButton: FloatingActionButton(
        onPressed: () async {
          await thread.generateContent([
            AIContent.text("Hello, Gemini!"),
          ]);
        },
        child: Icon(Icons.send),
      ),
    );
  }
}

Additional Resources

For detailed information on AIThread, AISingle, AIContent, AITool, and MCP integration, refer to the masamune_ai package documentation.

GitHub Sponsors

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https://github.com/sponsors/mathrunet