https://github.com/flutter/ai
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/flutter/ai
- Owner: flutter
- License: bsd-3-clause
- Created: 2024-09-12T20:44:25.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-03-29T00:20:30.000Z (4 months ago)
- Last Synced: 2025-03-29T01:25:11.430Z (4 months ago)
- Language: Dart
- Size: 2.2 MB
- Stars: 100
- Watchers: 44
- Forks: 31
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://github.com/flutter/ai/actions/workflows/flutter.yml)
[](https://pub.dev/packages/flutter_ai_toolkit)
[](https://pub.dev/packages/flutter_ai_toolkit/publisher)
Hello and welcome to the [Flutter AI Toolkit][]!
The AI Toolkit is a set of AI chat-related widgets to make it easy to
add an AI chat window to your Flutter app.
The AI Toolkit is organized around an abstract LLM provider API that makes it
easy to swap out the LLM provider that you'd like your chat provider to use.
Out of the box, it comes with support for two LLM provider integrations:
[Google Gemini AI][] and [Firebase Vertex AI][].
[Flutter AI Toolkit]: https://docs.flutter.dev/ai-toolkit
[Google Gemini AI]: https://ai.google.dev/gemini-api/docs
[Firebase Vertex AI]: https://firebase.google.com/docs/vertex-ai
## Key features
* **Multi-turn chat:**
Maintains context across multiple interactions.
* **Streaming responses:**
Displays AI responses in real-time as they're generated.
* **Rich text display:**
Supports formatted text in chat messages.
* **Voice input:**
Allows users to input prompts using speech.
* **Multimedia attachments:**
Enables sending and receiving various media types.
* **Custom styling:**
Offers extensive customization to match your app's design.
* **Chat serialization/deserialization:**
Store and retrieve conversations between app sessions.
* **Pluggable LLM support:**
Implement a simple interface to plug in your own LLM.
* **Cross-platform support:**
Compatible with the Android, iOS, web, and macOS platforms.
## Online demo
To see the AI toolkit in action, check out the [online demo][].
The [demo's source code][] can be a useful reference as you integrate
the AI toolkit in to your own applications.
[online demo]: https://flutter-ai-toolkit-examp-60bad.web.app/
[demo's source code]: https://github.com/flutter/ai/blob/main/example/lib/demo/demo.dart
## Getting started
1. **Installation**
Add the following dependencies to your `pubspec.yaml` file:
```sh
$ flutter pub add flutter_ai_toolkit google_generative_ai firebase_core
```
2. **Vertex AI configuration**
The AI toolkit supports both Firebase Vertex AI and Google Gemini AI as LLM providers.
Firebase Vertex AI is the preferred approach for production use cases.
To use Vertex AI in your project, follow the steps described in
[Get started using the Vertex AI in Firebase SDKs][vertex-ai-get-started].
Once that's complete, integrate the new Firebase project into
your Flutter app using the `flutterfire` CLI tool, as described in
[Add Firebase to your Flutter app][] docs.
After following these instructions, you're ready to
use Firebase Vertex AI in your Flutter app.
Start by initializing Firebase:
```dart
import 'package:firebase_core/firebase_core.dart';
import 'package:firebase_vertexai/firebase_vertexai.dart';
import 'package:flutter_ai_toolkit/flutter_ai_toolkit.dart';
// Other app imports...
import 'firebase_options.dart'; // Generated by `flutterfire config`.
void main() async {
WidgetsFlutterBinding.ensureInitialized();
await Firebase.initializeApp(options: DefaultFirebaseOptions.currentPlatform);
runApp(const App());
}
// Implementation of App and other widgets...
```
With Firebase properly initialized in your Flutter app,
you're now ready to create an instance of the Vertex provider:
```dart
class ChatPage extends StatelessWidget {
const ChatPage({super.key});
@override
Widget build(BuildContext context) => Scaffold(
appBar: AppBar(title: const Text(App.title)),
// Create the chat view, passing in the Vertex provider.
body: LlmChatView(
provider: VertexProvider(
chatModel: FirebaseVertexAI.instance.generativeModel(
model: 'gemini-1.5-flash',
),
),
),
);
}
```
The `FirebaseVertexAI` class comes from the `firebase_vertexai` package.
The AI Toolkit provides the `VertexProvider` class to
expose Vertex AI to the `LlmChatView`.
Note that you provide a model name that
corresponds to one of the [available model names][firebase-models],
but you don't provide an API key.
All of that is handled as part of the Firebase project.
For a complete usage example,
check out the [`vertex.dart` sample][].
3. **Gemini AI configuration**
To use Google Gemini AI, obtain an API key from [Google AI Studio][].
Be careful not to check this key into your source code repository to
prevent unauthorized access.
You'll also need to choose a specific Gemini model name to
use in creating an instance of the Gemini model.
The following example uses `gemini-1.5-flash`, but
you can choose from an [ever-expanding set of models][gemini models].
```dart
import 'package:flutter/material.dart';
import 'package:flutter_ai_toolkit/flutter_ai_toolkit.dart';
import 'package:google_generative_ai/google_generative_ai.dart';
void main() {
runApp(const MyApp());
}
class MyApp extends StatelessWidget {
const MyApp({super.key});
@override
Widget build(BuildContext context) {
return MaterialApp(
title: 'Flutter Demo',
theme: ThemeData(
colorScheme: ColorScheme.fromSeed(seedColor: Colors.deepPurple),
useMaterial3: true,
),
home: const ChatPage(title: 'Generative AI Chat'),
);
}
}
class ChatPage extends StatelessWidget {
const ChatPage({super.key, required this.title});
final String title;
@override
Widget build(BuildContext context) {
return Scaffold(
appBar: AppBar(title: Text(title)),
body: LlmChatView(
provider: GeminiProvider(
model: GenerativeModel(
model: 'gemini-1.5-flash',
apiKey: 'GEMINI-API-KEY',
),
),
),
);
}
}
```
The `GenerativeModel` class comes from the `google_generative_ai` package.
The AI Toolkit builds on top of this package with the `GeminiProvider`,
which plugs Gemini AI into the `LlmChatView`, the top-level widget that
enables an LLM-based chat conversation with your users.
For a complete usage example,
check out the [`gemini.dart` sample][].
While Gemini AI is useful for quick prototyping, the recommended solution
for production apps is [Vertex AI in Firebase][]. This eliminates the need
for an API key in your client app and replaces it with a more secure Firebase
project.
4. **Set up device permissions**
To enable your users to take advantage of features like voice input and
media attachments, ensure that your app has the necessary permissions:
- **Network access:**
To enable network access on macOS, add the following to your `*.entitlements` files:
```xml
...
com.apple.security.network.client
```
To enable network access on Android, ensure that your `AndroidManifest.xml` file contains the following:
```xml
...
```
- **Microphone access:**
To enable voice input for users, update configs according to the
[permission and setup instructions][record-setup] for `package:record`.
- **File selection:**
To enable users to select and attach files,
follow the [usage instructions][file-setup] for `package:file_selector`.
- **Image selection:**
To enable users to take or select a picture from their device, refer to
the [installation instructions][image-setup] for `package:image_picker`.
[gemini models]: https://ai.google.dev/gemini-api/docs/models/gemini
[`gemini.dart` sample]: https://github.com/flutter/ai/blob/main/example/lib/gemini/gemini.dart
[Vertex AI in Firebase]: https://firebase.google.com/products/vertex-ai-in-firebase
[vertex-ai-get-started]: https://firebase.google.com/docs/vertex-ai/get-started?platform=flutter
[Add Firebase to your Flutter app]: https://firebase.google.com/docs/flutter/setup
[firebase-models]: https://firebase.google.com/docs/vertex-ai/gemini-models#available-model-names
[`vertex.dart` sample]: https://github.com/flutter/ai/blob/main/example/lib/vertex/vertex.dart
[record-setup]: https://pub.dev/packages/record#setup-permissions-and-others
[file-setup]: https://pub.dev/packages/file_selector#usage
[image-setup]: https://pub.dev/packages/image_picker#installation
## Samples
To run the [example apps][] in the `example/lib` directory, first replace the
`example/lib/gemini_api_key.dart` and `example/lib/firebase_options.dart` files.
They're placeholders for configuration needed by the examples.
[example apps]: https://github.com/flutter/ai/tree/main/example/lib
### `gemini_api_key.dart`
Most of the example apps rely on a Gemini API key, so for those to work,
you'll need to add your API key to the `example/lib/gemini_api_key.dart` file.
You can get an API key in [Google AI Studio][].
> [!WARNING]
> Be careful not to check the `gemini_api_key.dart` file into your git repo.
### `firebase_options.dart`
To use the [Vertex AI example app][], add your
Firebase configuration details to the `example/lib/firebase_options.dart` file.
You can do this running the `flutterfire` CLI tool as described in
[Add Firebase to your Flutter app][] **from within the `example` directory**.
> [!WARNING]
> Be careful not to check the `firebase_options.dart` file into your git repo.
[Vertex AI example app]: https://github.com/flutter/ai/blob/main/example/lib/vertex/vertex.dart
[Add Firebase to your Flutter app]: https://firebase.google.com/docs/flutter/setup
## Feedback
Along the way, as you use this package, please
[log issues and feature requests][] as well as
submit any [code you'd like to contribute][].
We want your feedback and contributions to ensure that the AI Toolkit is
as robust and useful as it can be for your real-world apps.
[log issues and feature requests]: https://github.com/flutter/ai/issues
[code you'd like to contribute]: https://github.com/flutter/ai/pulls
[Google AI Studio]: https://aistudio.google.com/app/apikey