firebase-ai（Firebase AI 命令行）

Firebase AI Skill

This skill defines how to correctly use Firebase AI Logic in Flutter applications.

When to Use

Use this skill when:

• Setting up and configuring Firebase AI in a Flutter project.
• Generating text content or chat responses with Gemini models.
• Implementing streaming AI responses for real-time UI updates.
• Sending multimodal prompts (text + images) to Gemini.
• Handling errors, offline scenarios, and rate limits for AI operations.
• Applying security and privacy considerations for AI features.

1. Setup and Configuration

flutter pub add firebase_ai

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

// Initialize FirebaseApp
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service
final model =
    FirebaseAI.googleAI().generativeModel(model: 'gemini-2.5-flash');

• Ensure the Firebase project is configured for AI services via the Firebase AI Logic page in the Firebase Console.
• Initialize Firebase before using any Firebase AI features.
• Use FirebaseAI.googleAI() for the Gemini Developer API backend (recommended starting point).
• Implement App Check to prevent abuse of Firebase AI endpoints.

Platform support:

2. Generating Content

Single-turn text generation

final response = await model.generateContent([
  Content.text('Summarize the benefits of Flutter for mobile development'),
]);
final text = response.text; // The generated summary string

Multi-turn chat

final chat = model.startChat();
final response = await chat.sendMessage(
  Content.text('What is the difference between StatelessWidget and StatefulWidget?'),
);
print(response.text);

// Follow-up in the same conversation
final followUp = await chat.sendMessage(
  Content.text('When should I use StatefulWidget?'),
);
print(followUp.text);

Streaming responses

Use streaming to display partial results as they arrive:

final stream = model.generateContentStream([
  Content.text('Write a step-by-step guide to implementing dark mode in Flutter'),
]);

await for (final chunk in stream) {
  // Append chunk.text to the UI progressively
  setState(() => _output += chunk.text ?? '');
}

Multimodal prompts (text + image)

final imageBytes = await File('photo.jpg').readAsBytes();
final response = await model.generateContent([
  Content.multi([
    TextPart('Describe what you see in this image'),
    InlineDataPart('image/jpeg', imageBytes),
  ]),
]);

3. Error Handling

Wrap AI calls in structured error handling:

try {
  final response = await model.generateContent([Content.text(prompt)]);
  return response.text;
} on FirebaseAIException catch (e) {
  if (e.message?.contains('quota') ?? false) {
    // Handle rate limiting — show retry message or queue the request
    return 'Service is busy. Please try again shortly.';
  }
  return 'AI service error: ${e.message}';
} catch (e) {
  return 'Unexpected error: $e';
}

• Provide meaningful error messages to users when AI operations fail.
• Handle offline scenarios with appropriate fallback behavior (e.g., cached responses).
• Implement exponential backoff for rate-limited or transient errors.

4. Security and Privacy

• Follow Firebase Security Rules best practices when using AI services alongside other Firebase products.
• Ensure proper authentication and authorization for AI feature access.
• Sanitize user input before sending it to the model to prevent prompt injection.
• Be mindful of data privacy requirements when processing user content with AI services.
• Implement appropriate content filtering and moderation using safety settings:

final model = FirebaseAI.googleAI().generativeModel(
  model: 'gemini-2.5-flash',
  safetySettings: [
    SafetySetting(HarmCategory.harassment, HarmBlockThreshold.medium),
    SafetySetting(HarmCategory.dangerousContent, HarmBlockThreshold.high),
  ],
);

References

• Firebase AI Logic Flutter documentation