Using the Gemini API, you can build freeform conversations across
multiple turns. The Firebase AI Logic SDK simplifies the process by managing
the state of the conversation, so unlike with generateContent()
(or generateContentStream()), you don't have to store the conversation history
yourself.
Before you begin
|
Click your Gemini API provider to view provider-specific content and code on this page. |
If you haven't already, complete the
getting started guide, which describes how to
set up your Firebase project, connect your app to Firebase, add the SDK,
initialize the backend service for your chosen Gemini API provider, and
create a GenerativeModel instance.
For testing and iterating on your prompts and even getting a generated code snippet, we recommend using Google AI Studio.
Send a chat prompt request
|
Before trying this sample, complete the
Before you begin section of this guide
to set up your project and app. In that section, you'll also click a button for your chosen Gemini API provider so that you see provider-specific content on this page. |
To build a multi-turn conversation (like chat), start off by initializing the
chat by calling startChat(). Then use
sendMessage() to send a new user message, which
will also append the message and the response to the chat history.
There are two possible options for role associated with the content in a
conversation:
user: the role which provides the prompts. This value is the default for calls tosendMessage(), and the function throws an exception if a different role is passed.model: the role which provides the responses. This role can be used when callingstartChat()with existinghistory.
Swift
You can call
startChat()
and
sendMessage()
to send a new user message:
import FirebaseAI
// Initialize the Gemini Developer API backend service
let ai = FirebaseAI.firebaseAI(backend: .googleAI())
// Create a `GenerativeModel` instance with a model that supports your use case
let model = ai.generativeModel(modelName: "gemini-2.0-flash")
// Optionally specify existing chat history
let history = [
ModelContent(role: "user", parts: "Hello, I have 2 dogs in my house."),
ModelContent(role: "model", parts: "Great to meet you. What would you like to know?"),
]
// Initialize the chat with optional chat history
let chat = model.startChat(history: history)
// To generate text output, call sendMessage and pass in the message
let response = try await chat.sendMessage("How many paws are in my house?")
print(response.text ?? "No text in response.")
Kotlin
You can call startChat()
and
sendMessage()
to send a new user message:
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
.generativeModel("gemini-2.0-flash")
// Initialize the chat
val chat = generativeModel.startChat(
history = listOf(
content(role = "user") { text("Hello, I have 2 dogs in my house.") },
content(role = "model") { text("Great to meet you. What would you like to know?") }
)
)
val response = chat.sendMessage("How many paws are in my house?")
print(response.text)
Java
You can call
startChat()
and
sendMessage()
to send a new user message:
ListenableFuture.
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
.generativeModel("gemini-2.0-flash");
// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);
// (optional) Create previous chat history for context
Content.Builder userContentBuilder = new Content.Builder();
userContentBuilder.setRole("user");
userContentBuilder.addText("Hello, I have 2 dogs in my house.");
Content userContent = userContentBuilder.build();
Content.Builder modelContentBuilder = new Content.Builder();
modelContentBuilder.setRole("model");
modelContentBuilder.addText("Great to meet you. What would you like to know?");
Content modelContent = userContentBuilder.build();
List<Content> history = Arrays.asList(userContent, modelContent);
// Initialize the chat
ChatFutures chat = model.startChat(history);
// Create a new user message
Content.Builder messageBuilder = new Content.Builder();
messageBuilder.setRole("user");
messageBuilder.addText("How many paws are in my house?");
Content message = messageBuilder.build();
// Send the message
ListenableFuture<GenerateContentResponse> response = chat.sendMessage(message);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
@Override
public void onSuccess(GenerateContentResponse result) {
String resultText = result.getText();
System.out.println(resultText);
}
@Override
public void onFailure(Throwable t) {
t.printStackTrace();
}
}, executor);
Web
You can call
startChat()
and
sendMessage()
to send a new user message:
import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";
// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
// ...
};
// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);
// Initialize the Gemini Developer API backend service
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });
// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(ai, { model: "gemini-2.0-flash" });
async function run() {
const chat = model.startChat({
history: [
{
role: "user",
parts: [{ text: "Hello, I have 2 dogs in my house." }],
},
{
role: "model",
parts: [{ text: "Great to meet you. What would you like to know?" }],
},
],
generationConfig: {
maxOutputTokens: 100,
},
});
const msg = "How many paws are in my house?";
const result = await chat.sendMessage(msg);
const response = await result.response;
const text = response.text();
console.log(text);
}
run();
Dart
You can call
startChat()
and
sendMessage()
to send a new user message:
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
// Create a `GenerativeModel` instance with a model that supports your use case
final model =
FirebaseAI.googleAI().generativeModel(model: 'gemini-2.0-flash');
final chat = model.startChat();
// Provide a prompt that contains text
final prompt = [Content.text('Write a story about a magic backpack.')];
final response = await chat.sendMessage(prompt);
print(response.text);
Unity
You can call
startChat()
and
sendMessage()
to send a new user message:
using Firebase;
using Firebase.AI;
// Initialize the Gemini Developer API backend service
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());
// Create a `GenerativeModel` instance with a model that supports your use case
var model = ai.GetGenerativeModel(modelName: "gemini-2.0-flash");
// Optionally specify existing chat history
var history = new [] {
ModelContent.Text("Hello, I have 2 dogs in my house."),
new ModelContent("model", new ModelContent.TextPart("Great to meet you. What would you like to know?")),
};
// Initialize the chat with optional chat history
var chat = model.StartChat(history);
// To generate text output, call SendMessageAsync and pass in the message
var response = await chat.SendMessageAsync("How many paws are in my house?");
UnityEngine.Debug.Log(response.Text ?? "No text in response.");
Learn how to choose a model appropriate for your use case and app.
Stream the response
|
Before trying this sample, complete the
Before you begin section of this guide
to set up your project and app. In that section, you'll also click a button for your chosen Gemini API provider so that you see provider-specific content on this page. |
You can achieve faster interactions by not waiting for the entire result from
the model generation, and instead use streaming to handle partial results.
To stream the response, call sendMessageStream().
Swift
You can call
startChat()
and
sendMessageStream()
to stream responses from the model:
import FirebaseAI
// Initialize the Gemini Developer API backend service
let ai = FirebaseAI.firebaseAI(backend: .googleAI())
// Create a `GenerativeModel` instance with a model that supports your use case
let model = ai.generativeModel(modelName: "gemini-2.0-flash")
// Optionally specify existing chat history
let history = [
ModelContent(role: "user", parts: "Hello, I have 2 dogs in my house."),
ModelContent(role: "model", parts: "Great to meet you. What would you like to know?"),
]
// Initialize the chat with optional chat history
let chat = model.startChat(history: history)
// To stream generated text output, call sendMessageStream and pass in the message
let contentStream = try chat.sendMessageStream("How many paws are in my house?")
for try await chunk in contentStream {
if let text = chunk.text {
print(text)
}
}
Kotlin
You can call
startChat()
and
sendMessageStream()
to stream responses from the model:
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
.generativeModel("gemini-2.0-flash")
// Initialize the chat
val chat = generativeModel.startChat(
history = listOf(
content(role = "user") { text("Hello, I have 2 dogs in my house.") },
content(role = "model") { text("Great to meet you. What would you like to know?") }
)
)
chat.sendMessageStream("How many paws are in my house?").collect { chunk ->
print(chunk.text)
}
Java
You can call
startChat()
and
sendMessageStream()
to stream responses from the model:
Publisher type from the Reactive Streams library.
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
.generativeModel("gemini-2.0-flash");
// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);
// (optional) Create previous chat history for context
Content.Builder userContentBuilder = new Content.Builder();
userContentBuilder.setRole("user");
userContentBuilder.addText("Hello, I have 2 dogs in my house.");
Content userContent = userContentBuilder.build();
Content.Builder modelContentBuilder = new Content.Builder();
modelContentBuilder.setRole("model");
modelContentBuilder.addText("Great to meet you. What would you like to know?");
Content modelContent = userContentBuilder.build();
List<Content> history = Arrays.asList(userContent, modelContent);
// Initialize the chat
ChatFutures chat = model.startChat(history);
// Create a new user message
Content.Builder messageBuilder = new Content.Builder();
messageBuilder.setRole("user");
messageBuilder.addText("How many paws are in my house?");
Content message = messageBuilder.build();
// Send the message
Publisher<GenerateContentResponse> streamingResponse =
chat.sendMessageStream(message);
final String[] fullResponse = {""};
streamingResponse.subscribe(new Subscriber<GenerateContentResponse>() {
@Override
public void onNext(GenerateContentResponse generateContentResponse) {
String chunk = generateContentResponse.getText();
fullResponse[0] += chunk;
}
@Override
public void onComplete() {
System.out.println(fullResponse[0]);
}
// ... other methods omitted for brevity
});
Web
You can call
startChat()
and
sendMessageStream()
to stream responses from the model:
import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";
// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
// ...
};
// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);
// Initialize the Gemini Developer API backend service
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });
// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(ai, { model: "gemini-2.0-flash" });
async function run() {
const chat = model.startChat({
history: [
{
role: "user",
parts: [{ text: "Hello, I have 2 dogs in my house." }],
},
{
role: "model",
parts: [{ text: "Great to meet you. What would you like to know?" }],
},
],
generationConfig: {
maxOutputTokens: 100,
},
});
const msg = "How many paws are in my house?";
const result = await chat.sendMessageStream(msg);
let text = '';
for await (const chunk of result.stream) {
const chunkText = chunk.text();
console.log(chunkText);
text += chunkText;
}
}
run();
Dart
You can call
startChat()
and
sendMessageStream()
to stream responses from the model:
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
// Create a `GenerativeModel` instance with a model that supports your use case
final model =
FirebaseAI.googleAI().generativeModel(model: 'gemini-2.0-flash');
final chat = model.startChat();
// Provide a prompt that contains text
final prompt = [Content.text('Write a story about a magic backpack.')];
final response = await chat.sendMessageStream(prompt);
await for (final chunk in response) {
print(chunk.text);
}
Unity
You can call
startChat()
and
sendMessageStream()
to stream responses from the model:
using Firebase;
using Firebase.AI;
// Initialize the Gemini Developer API backend service
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());
// Create a `GenerativeModel` instance with a model that supports your use case
var model = ai.GetGenerativeModel(modelName: "gemini-2.0-flash");
// Optionally specify existing chat history
var history = new [] {
ModelContent.Text("Hello, I have 2 dogs in my house."),
new ModelContent("model", new ModelContent.TextPart("Great to meet you. What would you like to know?")),
};
// Initialize the chat with optional chat history
var chat = model.StartChat(history);
// To stream generated text output, call SendMessageStreamAsync and pass in the message
var responseStream = chat.SendMessageStreamAsync("How many paws are in my house?");
await foreach (var response in responseStream) {
if (!string.IsNullOrWhiteSpace(response.Text)) {
UnityEngine.Debug.Log(response.Text);
}
}
What else can you do?
- Learn how to count tokens before sending long prompts to the model.
- Set up Cloud Storage for Firebase so that you can include large files in your multimodal requests and have a more managed solution for providing files in prompts. Files can include images, PDFs, video, and audio.
-
Start thinking about preparing for production (see the
production checklist),
including:
- Setting up Firebase App Check to protect the Gemini API from abuse by unauthorized clients.
- Integrating Firebase Remote Config to update values in your app (like model name) without releasing a new app version.
Try out other capabilities
- Generate text from text-only prompts.
- Generate text by prompting with various file types, like images, PDFs, video, and audio.
- Generate structured output (like JSON) from both text and multimodal prompts.
- Generate images from text 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 (for Gemini) or aspect ratio and person generation (for Imagen).
- Use safety settings to adjust the likelihood of getting responses that may be considered harmful.
Learn more about the supported models
Learn about the models available for various use cases and their quotas and pricing.Give feedback about your experience with Firebase AI Logic