The latest Gemini models, like Gemini 3.5 Flash, are available to use with Firebase AI Logic! Learn more.

Gemini 2.0 Flash and Flash-Lite models were shut down on June 1, 2026. To avoid service disruption, update to a newer model like gemini-3.1-flash-lite. Learn more.

All Imagen models will shut down on June 24, 2026. Learn about migrating your apps to use Nano Banana.

Google uses AI technology to translate content into your preferred language. AI translations can contain errors.

使用 Gemini API 构建多轮对话（聊天）

使用 Gemini API，您可以构建多轮自由对话。Firebase AI Logic SDK 通过管理对话状态来简化流程，因此与 generateContent()（或 generateContentStream()）不同，您无需自行存储对话记录。

跳转到纯文本聊天对应的代码跳转到迭代式图片编辑对应的代码跳转到流式响应对应的代码

准备工作

点击您的 Gemini API 提供商，以查看此页面上特定于提供商的内容和代码。

如果您尚未完成入门指南，请先完成该指南。该指南介绍了如何设置 Firebase 项目、将应用连接到 Firebase、添加 SDK、为所选的 Gemini API 提供方初始化后端服务，以及创建 GenerativeModel 实例。

如需测试和迭代提示，我们建议使用 Google AI Studio。

查看实用资源

Swift

试用快速入门应用

使用快速入门应用可快速试用 SDK，并查看各种使用情形的完整实现。如果您没有自己的 Apple 平台应用，也可以使用快速入门应用。如需使用快速入门应用，您需要将其关联到 Firebase 项目。

前往快速入门应用

观看视频教程

此视频演示了如何通过构建一个真实的 AI 赋能的膳食规划应用来实现与 Firebase AI Logic 的对话，该应用允许用户与厨房厨师就他们想准备的任何食谱进行对话。

您还可以下载并探索视频中应用的代码库。

查看视频中应用的代码库

Kotlin

试用快速入门应用

使用快速入门应用可快速试用 SDK，并查看各种使用情形的完整实现。如果您没有自己的 Android 应用，也可以使用快速入门应用。如要使用快速入门应用，您需要将其关联到 Firebase 项目。

前往快速入门应用

Java

试用快速入门应用

前往快速入门应用

Web

试用快速入门应用

使用快速入门应用可快速试用 SDK，并查看各种使用情形的完整实现。如果您没有自己的 Web 应用，也可以使用快速入门应用。若要使用快速入门应用，您需要将其关联到 Firebase 项目。

前往快速入门应用

观看视频教程

您还可以下载并探索视频中应用的代码库。

查看视频中应用的代码库

Dart

试用快速入门应用

使用快速入门应用可快速试用 SDK，并查看各种使用情形的完整实现。如果您没有自己的 Flutter 应用，也可以使用快速入门应用。如要使用快速入门应用，您需要将其关联到 Firebase 项目。

前往快速入门应用

Unity

试用快速入门应用

使用快速入门应用可快速试用 SDK，并查看各种使用情形的完整实现。如果您没有自己的 Unity 游戏，也可以使用快速入门应用。如需使用快速入门应用，您需要将其关联到 Firebase 项目。

前往快速入门应用

打造纯文本聊天体验

在试用此示例之前，请完成本指南的准备工作部分，以设置您的项目和应用。
在该部分中，您还需要点击所选Gemini API提供商对应的按钮，以便在本页上看到特定于提供商的内容。

如需构建多轮对话（例如聊天），请先调用 startChat() 初始化聊天。然后使用 sendMessage() 发送新的用户消息，该消息和回答也会附加到聊天记录中。

对于对话中的内容，role 有两种可能的选项：

user：提供提示的角色。此值是对 sendMessage() 的调用的默认值，如果传递了其他角色，该函数会抛出异常。
model：提供回答的角色。当使用现有 history 调用 startChat() 时，可以使用此角色。

Swift

您可以调用 startChat() 和 sendMessage() 来发送新用户消息：


import FirebaseAILogic

// 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-3.5-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

您可以调用 startChat() 和 sendMessage() 来发送新用户消息：

^{对于 Kotlin，此 SDK 中的方法是挂起函数，需要从 Coroutine 范围调用。}


// 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-3.5-flash")


// Initialize the chat
val chat = model.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

您可以调用 startChat() 和 sendMessage() 来发送新用户消息：

^{对于 Java，此 SDK 中的方法会返回 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-3.5-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

您可以调用 startChat() 和 sendMessage() 来发送新用户消息：


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-3.5-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 text = result.response.text();
  console.log(text);
}

run();

Dart

您可以调用 startChat() 和 sendMessage() 来发送新用户消息：


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-3.5-flash');


// Initialize the chat with history
final chat = model.startChat(
  history: [
    Content.text('Hello, I have 2 dogs in my house.'),
    Content.model([const TextPart('Great to meet you. What would you like to know?')]),
  ],
);
// Send a message to the chat
final response = await chat.sendMessage(Content.text('How many paws are in my house?'));
print(response.text);

Unity

您可以调用 StartChat() 和 SendMessageAsync() 来发送新用户消息：


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-3.5-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.");

了解如何选择适合您的应用场景和应用的模型。

使用多轮对话迭代和修改图片

通过多轮对话，您可以与 Gemini 模型就其生成的图片或您提供的图片进行迭代。

请务必创建 GenerativeModel 实例，在模型配置中包含 TEXT 和 IMAGE 的响应模态（如果您只想要图片输出，则排除 TEXT），并调用 startChat() 和 sendMessage() 以发送新的用户消息。

Swift


import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
let generativeModel = FirebaseAI.firebaseAI(backend: .googleAI()).generativeModel(
  modelName: "gemini-2.5-flash-image",
  // Configure the model to respond with text and images (required).
  generationConfig: GenerationConfig(responseModalities: [.text, .image])
)

// Initialize the chat
let chat = model.startChat()

guard let image = UIImage(named: "scones") else { fatalError("Image file not found.") }

// Provide an initial text prompt instructing the model to edit the image
let prompt = "Edit this image to make it look like a cartoon"

// To generate an initial response, send a user message with the image and text prompt
let response = try await chat.sendMessage(image, prompt)

// Inspect the generated image
guard let inlineDataPart = response.inlineDataParts.first else {
  fatalError("No image data in response.")
}
guard let uiImage = UIImage(data: inlineDataPart.data) else {
  fatalError("Failed to convert data to UIImage.")
}

// Follow up requests do not need to specify the image again
let followUpResponse = try await chat.sendMessage("But make it old-school line drawing style")

// Inspect the edited image after the follow up request
guard let followUpInlineDataPart = followUpResponse.inlineDataParts.first else {
  fatalError("No image data in response.")
}
guard let followUpUIImage = UIImage(data: followUpInlineDataPart.data) else {
  fatalError("Failed to convert data to UIImage.")
}

Kotlin


// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
    modelName = "gemini-2.5-flash-image",
    // Configure the model to respond with text and images (required)
    generationConfig = generationConfig {
responseModalities = listOf(ResponseModality.TEXT, ResponseModality.IMAGE) }
)

// Provide an image for the model to edit
val bitmap = BitmapFactory.decodeResource(context.resources, R.drawable.scones)

// Create the initial prompt instructing the model to edit the image
val prompt = content {
    image(bitmap)
    text("Edit this image to make it look like a cartoon")
}

// Initialize the chat
val chat = model.startChat()

// To generate an initial response, send a user message with the image and text prompt
var response = chat.sendMessage(prompt)
// Inspect the returned image
var generatedImageAsBitmap = response
    .candidates.first().content.parts.filterIsInstance<ImagePart>().firstOrNull()?.image

// Follow up requests do not need to specify the image again
response = chat.sendMessage("But make it old-school line drawing style")
generatedImageAsBitmap = response
    .candidates.first().content.parts.filterIsInstance<ImagePart>().firstOrNull()?.image

Java


// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI()).generativeModel(
    "gemini-2.5-flash-image",
    // Configure the model to respond with text and images (required)
    new GenerationConfig.Builder()
        .setResponseModalities(Arrays.asList(ResponseModality.TEXT, ResponseModality.IMAGE))
        .build()
);

GenerativeModelFutures model = GenerativeModelFutures.from(ai);

// Provide an image for the model to edit
Bitmap bitmap = BitmapFactory.decodeResource(resources, R.drawable.scones);

// Initialize the chat
ChatFutures chat = model.startChat();

// Create the initial prompt instructing the model to edit the image
Content prompt = new Content.Builder()
        .setRole("user")
        .addImage(bitmap)
        .addText("Edit this image to make it look like a cartoon")
        .build();

// To generate an initial response, send a user message with the image and text prompt
ListenableFuture<GenerateContentResponse> response = chat.sendMessage(prompt);
// Extract the image from the initial response
ListenableFuture<@Nullable Bitmap> initialRequest = Futures.transform(response, result -> {
    for (Part part : result.getCandidates().get(0).getContent().getParts()) {
        if (part instanceof ImagePart) {
            ImagePart imagePart = (ImagePart) part;
            return imagePart.getImage();
        }
    }
    return null;
}, executor);

// Follow up requests do not need to specify the image again
ListenableFuture<GenerateContentResponse> modelResponseFuture = Futures.transformAsync(
        initialRequest,
        generatedImage -> {
            Content followUpPrompt = new Content.Builder()
                    .addText("But make it old-school line drawing style")
                    .build();
            return chat.sendMessage(followUpPrompt);
        },
        executor);

// Add a final callback to check the reworked image
Futures.addCallback(modelResponseFuture, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) {
        for (Part part : result.getCandidates().get(0).getContent().getParts()) {
            if (part instanceof ImagePart) {
                ImagePart imagePart = (ImagePart) part;
                Bitmap generatedImageAsBitmap = imagePart.getImage();
                break;
            }
        }
    }

    @Override
    public void onFailure(Throwable t) {
        t.printStackTrace();
    }
}, executor);

Web


import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend, ResponseModality } 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-2.5-flash-image",
  // Configure the model to respond with text and images (required)
  generationConfig: {
    responseModalities: [ResponseModality.TEXT, ResponseModality.IMAGE],
  },
});

// Prepare an image for the model to edit
async function fileToGenerativePart(file) {
  const base64EncodedDataPromise = new Promise((resolve) => {
    const reader = new FileReader();
    reader.onloadend = () => resolve(reader.result.split(',')[1]);
    reader.readAsDataURL(file);
  });
  return {
    inlineData: { data: await base64EncodedDataPromise, mimeType: file.type },
  };
}

const fileInputEl = document.querySelector("input[type=file]");
const imagePart = await fileToGenerativePart(fileInputEl.files[0]);

// Provide an initial text prompt instructing the model to edit the image
const prompt = "Edit this image to make it look like a cartoon";

// Initialize the chat
const chat = model.startChat();

// To generate an initial response, send a user message with the image and text prompt
const result = await chat.sendMessage([prompt, imagePart]);

// Request and inspect the generated image
try {
  const inlineDataParts = result.response.inlineDataParts();
  if (inlineDataParts?.[0]) {
    // Inspect the generated image
    const image = inlineDataParts[0].inlineData;
    console.log(image.mimeType, image.data);
  }
} catch (err) {
  console.error('Prompt or candidate was blocked:', err);
}

// Follow up requests do not need to specify the image again
const followUpResult = await chat.sendMessage("But make it old-school line drawing style");

// Request and inspect the returned image
try {
  const followUpInlineDataParts = followUpResult.response.inlineDataParts();
  if (followUpInlineDataParts?.[0]) {
    // Inspect the generated image
    const followUpImage = followUpInlineDataParts[0].inlineData;
    console.log(followUpImage.mimeType, followUpImage.data);
  }
} catch (err) {
  console.error('Prompt or candidate was blocked:', err);
}

Dart


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

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

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
final model = FirebaseAI.googleAI().generativeModel(
  model: 'gemini-2.5-flash-image',
  // Configure the model to respond with text and images (required).
  generationConfig: GenerationConfig(responseModalities: [ResponseModalities.text, ResponseModalities.image]),
);

// Prepare an image for the model to edit
final image = await File('scones.jpg').readAsBytes();
final imagePart = InlineDataPart('image/jpeg', image);

// Provide an initial text prompt instructing the model to edit the image
final prompt = TextPart("Edit this image to make it look like a cartoon");

// Initialize the chat
final chat = model.startChat();

// To generate an initial response, send a user message with the image and text prompt
final response = await chat.sendMessage([
  Content.multi([prompt,imagePart])
]);

// Inspect the returned image
if (response.inlineDataParts.isNotEmpty) {
  final imageBytes = response.inlineDataParts[0].bytes;
  // Process the image
} else {
  // Handle the case where no images were generated
  print('Error: No images were generated.');
}

// Follow up requests do not need to specify the image again
final followUpResponse = await chat.sendMessage([
  Content.text("But make it old-school line drawing style")
]);

// Inspect the returned image
if (followUpResponse.inlineDataParts.isNotEmpty) {
  final followUpImageBytes = response.inlineDataParts[0].bytes;
  // Process the image
} else {
  // Handle the case where no images were generated
  print('Error: No images were generated.');
}

Unity


using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a Gemini model that supports image output.
var model = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetGenerativeModel(
  modelName: "gemini-2.5-flash-image",
  // Configure the model to respond with text and images (required).
  generationConfig: new GenerationConfig(
    responseModalities: new[] { ResponseModality.Text, ResponseModality.Image })
);

// Prepare an image for the model to edit
var imageFile = System.IO.File.ReadAllBytes(System.IO.Path.Combine(
  UnityEngine.Application.streamingAssetsPath, "scones.jpg"));
var image = ModelContent.InlineData("image/jpeg", imageFile);

// Provide an initial text prompt instructing the model to edit the image
var prompt = ModelContent.Text("Edit this image to make it look like a cartoon.");

// Initialize the chat
var chat = model.StartChat();

// To generate an initial response, send a user message with the image and text prompt
var response = await chat.SendMessageAsync(new [] { prompt, image });

// Inspect the returned image
var imageParts = response.Candidates.First().Content.Parts
                         .OfType<ModelContent.InlineDataPart>()
                         .Where(part => part.MimeType == "image/png");
// Load the image into a Unity Texture2D object
UnityEngine.Texture2D texture2D = new(2, 2);
if (texture2D.LoadImage(imageParts.First().Data.ToArray())) {
  // Do something with the image
}

// Follow up requests do not need to specify the image again
var followUpResponse = await chat.SendMessageAsync("But make it old-school line drawing style");

// Inspect the returned image
var followUpImageParts = followUpResponse.Candidates.First().Content.Parts
                         .OfType<ModelContent.InlineDataPart>()
                         .Where(part => part.MimeType == "image/png");
// Load the image into a Unity Texture2D object
UnityEngine.Texture2D followUpTexture2D = new(2, 2);
if (followUpTexture2D.LoadImage(followUpImageParts.First().Data.ToArray())) {
  // Do something with the image
}

以流式传输回答

您可以不等待模型生成完整结果，而是使用流式传输来处理部分结果，从而实现更快的互动。如需流式传输响应，请调用 sendMessageStream()。

查看示例：流式传输聊天回答

Swift

您可以调用 startChat() 和 sendMessageStream() 来从模型中以流式传输响应：


import FirebaseAILogic

// 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-3.5-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

您可以调用 startChat() 和 sendMessageStream() 来从模型中以流式传输响应：

^{对于 Kotlin，此 SDK 中的方法是挂起函数，需要从 Coroutine 范围调用。}


// 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-3.5-flash")


// Initialize the chat
val chat = model.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

您可以调用 startChat() 和 sendMessageStream() 来从模型中以流式传输响应：

^{对于 Java，此 SDK 中的流式传输方法会返回 Reactive Streams 库中的 Publisher 类型。}


// 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-3.5-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

您可以调用 startChat() 和 sendMessageStream() 来从模型中以流式传输响应：


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-3.5-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

您可以调用 startChat() 和 sendMessageStream() 来从模型中以流式传输响应：


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-3.5-flash');


// Initialize the chat with history
final chat = model.startChat(
  history: [
    Content.text('Hello, I have 2 dogs in my house.'),
    Content.model([const TextPart('Great to meet you. What would you like to know?')]),
  ],
);
// Send a message to the chat
final response = await chat.sendMessageStream(Content.text('How many paws are in my house?'));
await for (final chunk in response) {
  print(chunk.text);
}

Unity

您可以调用 StartChat() 和 SendMessageStreamAsync() 来从模型中以流式传输响应：


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-3.5-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);
  }
}

了解如何选择适合您的应用场景和应用的模型。

您还可以做些什么？

了解如何在向模型发送长提示之前计算 token 数。
设置 Cloud Storage for Firebase，以便您可以在多模态请求中包含大型文件，并获得更易于管理的解决方案，在提示中提供文件。文件可以包括图片、PDF、视频和音频。
开始考虑为生产做准备（请参阅生产核对清单）：
- 尽早设置 Firebase App Check，以帮助保护 Gemini API 免遭未经授权的客户端滥用。
- 集成 Firebase Remote Config 以更新应用中的值（例如模型名称），而无需发布新的应用版本。

试用其他功能

根据纯文本提示生成文本。
通过各种文件类型（例如图片、PDF、视频和音频）提示生成文本。
根据文本提示和多模态提示生成结构化输出（例如 JSON）。
根据文本和多模态提示生成和修改图片。
使用工具（例如函数调用和依托 Google 搜索进行接地）将 Gemini 模型连接到应用的其余部分以及外部系统和信息。

了解如何控制内容生成

了解提示设计，包括最佳实践、策略和示例提示。
配置模型参数，例如输出 token 数上限、重复输出 token 的概率等。
使用安全设置来调整获得可能被视为有害的回答的可能性。

您还可以尝试使用提示和模型配置，甚至可以使用 Google AI Studio 获取生成的代码段。

详细了解支持的型号

了解适用于各种应用场景的模型及其配额和价格。

就您使用 Firebase AI Logic 的体验提供反馈

使用 Gemini API 构建多轮对话（聊天） 使用集合让一切井井有条 根据您的偏好保存内容并对其进行分类。

准备工作

查看实用资源

Swift

试用快速入门应用

观看视频教程

Kotlin

试用快速入门应用

Java

试用快速入门应用

Web

试用快速入门应用

观看视频教程

Dart

试用快速入门应用

Unity

试用快速入门应用

打造纯文本聊天体验

Swift

Kotlin

Java

Web

Dart

Unity

使用多轮对话迭代和修改图片

Swift

Kotlin

Java

Web

Dart

Unity

以流式传输回答

查看示例：流式传输聊天回答

Swift

Kotlin

Java

Web

Dart

Unity

您还可以做些什么？

试用其他功能

了解如何控制内容生成

详细了解支持的型号

使用 Gemini API 构建多轮对话（聊天）