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Get started with the Gemini API using the Vertex AI in Firebase SDKs

This guide shows you how to get started making calls to the Vertex AI Gemini API directly from your app using the Vertex AI in Firebase SDK for your chosen platform.

Other options for working with the Gemini API

Optionally experiment with an alternative "Google AI" version of the Gemini API
Get free-of-charge access (within limits and where available) using Google AI Studio and Google AI client SDKs. These SDKs should be used for prototyping only in mobile and web apps.

After you're familiar with how a Gemini API works, migrate to our Vertex AI in Firebase SDKs (this documentation), which have many additional features important for mobile and web apps, like protecting the API from abuse using Firebase App Check and support for large media files in requests.

Optionally call the Vertex AI Gemini API server-side (like with Python, Node.js, or Go)
Use the server-side Vertex AI SDKs, Firebase Genkit, or Firebase Extensions for the Gemini API.

Prerequisites

This guide assumes that you're familiar with developing apps with Flutter.

Make sure that your development environment and Flutter app meet the following requirements:
- Dart 3.2.0+
(Optional) Check out the sample app.

Download the sample app

You can try out the SDK quickly, see a complete implementation of various use cases, or use the sample app if don't have your own Flutter app. To use the sample app, you'll need to connect it to a Firebase project.

Step 1: Set up a Firebase project and connect your app to Firebase

If you already have a Firebase project and an app connected to Firebase

In the Firebase console, go to the Build with Gemini page.
Click the Vertex AI in Firebase card to launch a workflow that helps you complete the following tasks:
- Upgrade your project to use the pay-as-you-go Blaze pricing plan.
- Enable the required APIs in your project (Vertex AI API and Vertex AI in Firebase API).
Continue to the next step in this guide to add the SDK to your app.

If you do not already have a Firebase project and an app connected to Firebase

Set up a Firebase project

Sign into the Firebase console.
Click Create project, and then use either of the following options:
- Option 1: Create a wholly new Firebase project (and its underlying Google Cloud project automatically) by entering a new project name in the first step of the "Create project" workflow.
- Option 2: "Add Firebase" to an existing Google Cloud project by selecting your Google Cloud project name from the drop-down menu in the first step of the "Create project" workflow.
Note that when prompted, you do not need to set up Google Analytics to use the Vertex AI in Firebase SDKs.
In the Firebase console, go to the Build with Gemini page.
Click the Vertex AI in Firebase card to launch a workflow that helps you complete the following tasks:
- Upgrade your project to use the pay-as-you-go Blaze pricing plan.
- Enable the required APIs in your project (Vertex AI API and Vertex AI in Firebase API).

Connect your app to Firebase

Install the required command line tools:
1. If you haven't already, install the Firebase CLI.
2. Log into Firebase using your Google Account by running the following command:
```
firebase login
```
3. Install the FlutterFire CLI by running the following command from any directory:
```
dart pub global activate flutterfire_cli
```
Configure your apps to use Firebase:

Use the FlutterFire CLI to configure your Flutter apps to connect to Firebase.

From your Flutter project directory, run the following command to start the app configuration workflow:
```
flutterfire configure
```
What does this flutterfire configure workflow do?
The flutterfire configure workflow does the following:
- Asks you to select the platforms (iOS, Android, Web) supported in your Flutter app. For each selected platform, the FlutterFire CLI creates a new Firebase app in your Firebase project.
  
  You can select either to use an existing Firebase project or to create a new Firebase project. If you already have apps registered in an existing Firebase project, the FlutterFire CLI will attempt to match them based on your current Flutter project configuration.
- Creates a Firebase configuration file (firebase_options.dart) and adds it to the lib/ directory of your Flutter app.
In the next steps of this guide, you'll add the Vertex AI in Firebase SDK to your app and complete the required initialization specific to using the SDK and the Gemini API.

Step 2: Add the SDK

With your Firebase project set up and your app connected to Firebase (see previous step), you can now add the Vertex AI in Firebase SDK to your app.

The Vertex AI in Firebase plugin for Flutter (firebase_vertexai) provides access to the Vertex AI Gemini API.

From your Flutter project directory, run the following command to install the core plugin and the Vertex AI in Firebase plugin:
```
flutter pub add firebase_core && flutter pub add firebase_vertexai
```

In your lib/main.dart file, import the Firebase core plugin, the Vertex AI in Firebase plugin, and the configuration file you generated earlier:

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

Also in your lib/main.dart file, initialize Firebase using the DefaultFirebaseOptions object exported by the configuration file:
```
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);
```
Rebuild your Flutter application:
```
flutter run
```

Step 3: Initialize the Vertex AI service and the generative model

Before you can make any API calls, you need to initialize the Vertex AI service and the generative model.

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

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

// Initialize the Vertex AI service and the generative model
// Specify a model that supports your use case
// Gemini 1.5 models are versatile and can be used with all API capabilities
final model =
      FirebaseVertexAI.instance.generativeModel(model: 'gemini-1.5-flash');

When you've finished the getting started guide, learn how to choose a Gemini model and (optionally) a location appropriate for your use case and app.

Step 4: Call the Vertex AI Gemini API

Now that you've connected your app to Firebase, added the SDK, and initialized the Vertex AI service and the generative model, you're ready to call the Vertex AI Gemini API.

You can use generateContent() to generate text from a text-only prompt request:

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

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

// Initialize the Vertex AI service and the generative model
// Specify a model that supports your use case
// Gemini 1.5 models are versatile and can be used with all API capabilities
final model =
      FirebaseVertexAI.instance.generativeModel(model: 'gemini-1.5-flash');

// Provide a prompt that contains text
final prompt = [Content.text('Write a story about a magic backpack.')];

// To generate text output, call generateContent with the text input
final response = await model.generateContent(prompt);
print(response.text);

What else can you do?

Learn more about the Gemini models

Learn about the models available for various use cases and their quotas and pricing.

Try out other capabilities of the Gemini API

Learn more about generating text from text-only prompts, including how to stream the response.
Generate text from multimodal prompts (including text, images, PDFs, video, and audio).
Build multi-turn conversations (chat).
Generate structured output (like JSON) from both text and multimodal prompts.
Use function calling to connect generative models to external systems and information.

Learn how to control content generation

Understand prompt design, including best practices, strategies, and example prompts.
Configure model parameters like temperature and maximum output tokens.
Use safety settings to adjust the likelihood of getting responses that may be considered harmful.

You can also experiment with prompts and model configurations using Vertex AI Studio.

Give feedback about your experience with Vertex AI in Firebase