Live API 的功能


本页介绍了通过 Firebase AI Logic使用 Live API 时的功能，包括：

如需了解如何使用 各种 配置选项（ 例如添加转写或设置回答语音）自定义实现，请访问其他页面。您还可以了解 如何管理会话。


输入模态

本部分介绍了如何向 Live API 模型发送各种类型的输入。原生音频模型始终 需要音频输入（以及文本或视频输入的 可选附加模态），并且始终 以音频输出进行回答 。

请注意，Live API 还支持图片 + 音频输入。

流式传输音频输入

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。

Live API 最常见的功能是双向 音频串流，这意味着音频输入和输出的实时串流。

Live API 支持以下音频格式：

  • 输入音频格式 ：原始 16 位 PCM 音频，采样率 16kHz，小端序

  • 输出音频格式 ：原始 16 位 PCM 音频，采样率 24kHz，小端序

  • 支持的 MIME 类型：audio/x-aac、audio/flac、audio/mp3、 audio/m4a、audio/mpeg、audio/mpga、audio/mp4、audio/ogg、 audio/pcm、audio/wav、audio/webm

如需传达输入音频的采样率，请将每个包含音频的 Blob 的 MIME 类型设置为类似 audio/pcm;rate=16000 的值。

Swift

如需使用 Live API，请创建 LiveModel 实例，并将 回答模态 设置为 audio。


import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
// Create a `liveModel` instance with a model that supports the Live API.
let liveModel = FirebaseAI.firebaseAI(backend: .googleAI()).liveModel(
  modelName: "GEMINI_LIVE_API_MODEL_NAME",
  // Configure the model to respond with audio.
  generationConfig: LiveGenerationConfig(
    responseModalities: [.audio]
  )
)

do {
  let session = try await liveModel.connect()

  // Load the audio file, or tap a microphone.
  guard let audioFile = NSDataAsset(name: "audio.pcm") else {
    fatalError("Failed to load audio file")
  }

  // Provide the audio data.
  await session.sendAudioRealtime(audioFile.data)

  var outputText = ""
  for try await message in session.responses {
    if case let .content(content) = message.payload {
      content.modelTurn?.parts.forEach { part in
        if let part = part as? InlineDataPart, part.mimeType.starts(with: "audio/pcm") {
          // Handle 16bit pcm audio data at 24khz
          playAudio(part.data)
        }
      }
      // Optional: if you don't need to send more requests.
      if content.isTurnComplete {
        await session.close()
      }
    }
  }
} catch {
  fatalError(error.localizedDescription)
}

Kotlin

如需使用 Live API，请创建 LiveModel 实例，并将 回答模态 设置为 AUDIO。


// Initialize the Gemini Developer API backend service.
// Create a `liveModel` instance with a model that supports the Live API.
val liveModel = Firebase.ai(backend = GenerativeBackend.googleAI()).liveModel(
    modelName = "GEMINI_LIVE_API_MODEL_NAME",
    // Configure the model to respond with audio.
    generationConfig = liveGenerationConfig {
        responseModality = ResponseModality.AUDIO
   }
)

val session = liveModel.connect()

// This is the recommended approach.
// However, you can create your own recorder and handle the stream.
session.startAudioConversation()

Java

如需使用 Live API，请创建 LiveModel 实例，并将 回答模态 设置为 AUDIO。


ExecutorService executor = Executors.newFixedThreadPool(1);
// Initialize the Gemini Developer API backend service.
// Create a `liveModel` instance with a model that supports the Live API.
LiveGenerativeModel lm = FirebaseAI.getInstance(GenerativeBackend.googleAI()).liveModel(
        "GEMINI_LIVE_API_MODEL_NAME",
        // Configure the model to respond with audio.
        new LiveGenerationConfig.Builder()
                .setResponseModality(ResponseModality.AUDIO)
                .build()
);
LiveModelFutures liveModel = LiveModelFutures.from(lm);

ListenableFuture<LiveSession> sessionFuture =  liveModel.connect();

Futures.addCallback(sessionFuture, new FutureCallback<LiveSession>() {
    @Override
    public void onSuccess(LiveSession ses) {
	 LiveSessionFutures session = LiveSessionFutures.from(ses);
        session.startAudioConversation();
    }
    @Override
    public void onFailure(Throwable t) {
        // Handle exceptions
    }
}, executor);

Web

如需使用 Live API，请创建 LiveGenerativeModel 实例，并将 回答模态 设置为 AUDIO。


import { initializeApp } from "firebase/app";
import { getAI, getLiveGenerativeModel, 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 `LiveGenerativeModel` instance with a model that supports the Live API.
const liveModel = getLiveGenerativeModel(ai, {
  model: "GEMINI_LIVE_API_MODEL_NAME",
  // Configure the model to respond with audio.
  generationConfig: {
    responseModalities: [ResponseModality.AUDIO],
  },
});

const session = await liveModel.connect();

// Start the audio conversation.
const audioConversationController = await startAudioConversation(session);

// ... Later, to stop the audio conversation
// await audioConversationController.stop()

Dart

如需使用 Live API，请创建 LiveGenerativeModel 实例，并将 回答模态 设置为 audio。


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

late LiveModelSession _session;
final _audioRecorder = YourAudioRecorder();

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

// Initialize the Gemini Developer API backend service.
// Create a `liveGenerativeModel` instance with a model that supports the Live API.
final liveModel = FirebaseAI.googleAI().liveGenerativeModel(
  model: 'GEMINI_LIVE_API_MODEL_NAME',
  // Configure the model to respond with audio.
  liveGenerationConfig: LiveGenerationConfig(
    responseModalities: [ResponseModalities.audio],
  ),
);

_session = await liveModel.connect();

final audioRecordStream = _audioRecorder.startRecordingStream();
// Map the Uint8List stream to InlineDataPart stream.
final mediaChunkStream = audioRecordStream.map((data) {
  return InlineDataPart('audio/pcm', data);
});
await _session.startMediaStream(mediaChunkStream);

// In a separate thread, receive the audio response from the model.
await for (final message in _session.receive()) {
   // Process the received message.
}

Unity

如需使用 Live API，请创建 LiveModel 实例，并将 回答模态 设置为 Audio。


using Firebase;
using Firebase.AI;

async Task SendTextReceiveAudio() {
  // Initialize the Gemini Developer API backend service.
  // Create a `LiveModel` instance with a model that supports the Live API.
  var liveModel = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetLiveModel(
      modelName: "GEMINI_LIVE_API_MODEL_NAME",
      // Configure the model to respond with audio.
      liveGenerationConfig: new LiveGenerationConfig(
          responseModalities: new[] { ResponseModality.Audio })
    );

  LiveSession session = await liveModel.ConnectAsync();

  // Start a coroutine to send audio from the Microphone.
  var recordingCoroutine = StartCoroutine(SendAudio(session));

  // Start receiving the response.
  await ReceiveAudio(session);
}

IEnumerator SendAudio(LiveSession liveSession) {
  string microphoneDeviceName = null;
  int recordingFrequency = 16000;
  int recordingBufferSeconds = 2;

  var recordingClip = Microphone.Start(microphoneDeviceName, true,
                                       recordingBufferSeconds, recordingFrequency);

  int lastSamplePosition = 0;
  while (true) {
    if (!Microphone.IsRecording(microphoneDeviceName)) {
      yield break;
    }

    int currentSamplePosition = Microphone.GetPosition(microphoneDeviceName);

    if (currentSamplePosition != lastSamplePosition) {
      // The Microphone uses a circular buffer, so we need to check if the
      // current position wrapped around to the beginning, and handle it accordingly.
      int sampleCount;
      if (currentSamplePosition > lastSamplePosition) {
        sampleCount = currentSamplePosition - lastSamplePosition;
      } else {
        sampleCount = recordingClip.samples - lastSamplePosition + currentSamplePosition;
      }

      if (sampleCount > 0) {
        // Get the audio chunk.
        float[] samples = new float[sampleCount];
        recordingClip.GetData(samples, lastSamplePosition);

        // Send the data, discarding the resulting Task to avoid the warning.
        _ = liveSession.SendAudioAsync(samples);

        lastSamplePosition = currentSamplePosition;
      }
    }

    // Wait for a short delay before reading the next sample from the Microphone.
    const float MicrophoneReadDelay = 0.5f;
    yield return new WaitForSeconds(MicrophoneReadDelay);
  }
}

Queue<float> audioBuffer = new();

async Task ReceiveAudio(LiveSession liveSession) {
  int sampleRate = 24000;
  int channelCount = 1;

  // Create a looping AudioClip to fill with the received audio data.
  int bufferSamples = (int)(sampleRate * channelCount);
  AudioClip clip = AudioClip.Create("StreamingPCM", bufferSamples, channelCount,
                                    sampleRate, true, OnAudioRead);

  // Attach the clip to an AudioSource and start playing it.
  AudioSource audioSource = GetComponent<AudioSource>();
  audioSource.clip = clip;
  audioSource.loop = true;
  audioSource.Play();

  // Start receiving the response.
  await foreach (var message in liveSession.ReceiveAsync()) {
    // Process the received message
    foreach (float[] pcmData in message.AudioAsFloat) {
      lock (audioBuffer) {
        foreach (float sample in pcmData) {
          audioBuffer.Enqueue(sample);
        }
      }
    }
  }
}

// This method is called by the AudioClip to load audio data.
private void OnAudioRead(float[] data) {
  int samplesToProvide = data.Length;
  int samplesProvided = 0;

  lock(audioBuffer) {
    while (samplesProvided < samplesToProvide && audioBuffer.Count > 0) {
      data[samplesProvided] = audioBuffer.Dequeue();
      samplesProvided++;
    }
  }

  while (samplesProvided < samplesToProvide) {
    data[samplesProvided] = 0.0f;
    samplesProvided++;
  }
}

流式传输文本 + 音频输入

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。

如果需要，您可以发送 文本 输入以及音频输入，并接收流式 音频 输出。

Swift

如需使用 Live API，请创建 LiveModel 实例，并将 回答模态 设置为 audio。


import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
// Create a `liveModel` instance with a model that supports the Live API.
let liveModel = FirebaseAI.firebaseAI(backend: .googleAI()).liveModel(
  modelName: "GEMINI_LIVE_API_MODEL_NAME",
  // Configure the model to respond with audio.
  generationConfig: LiveGenerationConfig(
    responseModalities: [.audio]
  )
)

do {
  let session = try await liveModel.connect()

  // Provide a text prompt.
  let text = "tell a short story"

  await session.sendTextRealtime(text)

  var outputText = ""
  for try await message in session.responses {
    if case let .content(content) = message.payload {
      content.modelTurn?.parts.forEach { part in
        if let part = part as? InlineDataPart, part.mimeType.starts(with: "audio/pcm") {
          // Handle 16-bit PCM audio data at 24 kHz.
          playAudio(part.data)
        }
      }
      // Optional: if you don't need to send more requests.
      if content.isTurnComplete {
        await session.close()
      }
    }
  }
} catch {
  fatalError(error.localizedDescription)
}

Kotlin

如需使用 Live API，请创建 LiveModel 实例，并将 回答模态 设置为 AUDIO。


// Initialize the Gemini Developer API backend service.
// Create a `liveModel` instance with a model that supports the Live API.
val liveModel = Firebase.ai(backend = GenerativeBackend.googleAI()).liveModel(
    modelName = "GEMINI_LIVE_API_MODEL_NAME",
    // Configure the model to respond with audio.
    generationConfig = liveGenerationConfig {
        responseModality = ResponseModality.AUDIO
   }
)

val session = liveModel.connect()

// Provide a text prompt.
val text = "tell a short story"

session.send(text)

session.receive().collect {
    if(it.turnComplete) {
        // Optional: if you don't need to send more requests.
        session.stopReceiving();
    }
    // Handle 16bit pcm audio data at 24khz.
    playAudio(it.data)
}

Java

如需使用 Live API，请创建 LiveModel 实例，并将 回答模态 设置为 AUDIO。


ExecutorService executor = Executors.newFixedThreadPool(1);
// Initialize the Gemini Developer API backend service.
// Create a `liveModel` instance with a model that supports the Live API.
LiveGenerativeModel lm = FirebaseAI.getInstance(GenerativeBackend.googleAI()).liveModel(
        "GEMINI_LIVE_API_MODEL_NAME",
        // Configure the model to respond with audio.
        new LiveGenerationConfig.Builder()
                .setResponseModality(ResponseModality.AUDIO)
                .build()
);
LiveModelFutures model = LiveModelFutures.from(lm);
ListenableFuture<LiveSession> sessionFuture =  model.connect();
class LiveContentResponseSubscriber implements Subscriber<LiveContentResponse> {
    @Override
    public void onSubscribe(Subscription s) {
        s.request(Long.MAX_VALUE); // Request an unlimited number of items
    }
    @Override
    public void onNext(LiveContentResponse liveContentResponse) {
        // Handle 16bit pcm audio data at 24khz.
	liveContentResponse.getData();
    }
    @Override
    public void onError(Throwable t) {
        System.err.println("Error: " + t.getMessage());
    }
    @Override
    public void onComplete() {
        System.out.println("Done receiving messages!");
    }
}
Futures.addCallback(sessionFuture, new FutureCallback<LiveSession>() {
    @Override
    public void onSuccess(LiveSession ses) {
	 LiveSessionFutures session = LiveSessionFutures.from(ses);
        // Provide a text prompt.
        String text = "tell me a short story?";
        session.send(text);
        Publisher<LiveContentResponse> publisher = session.receive();
        publisher.subscribe(new LiveContentResponseSubscriber());
    }
    @Override
    public void onFailure(Throwable t) {
        // Handle exceptions
    }
}, executor);

Web

如需使用 Live API，请创建 LiveGenerativeModel 实例，并将 回答模态 设置为 AUDIO。


import { initializeApp } from "firebase/app";
import { getAI, getLiveGenerativeModel, 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 `LiveGenerativeModel` instance with a model that supports the Live API.
const liveModel = getLiveGenerativeModel(ai, {
  model: "GEMINI_LIVE_API_MODEL_NAME",
  // Configure the model to respond with audio
  generationConfig: {
    responseModalities: [ResponseModality.AUDIO],
  },
});

const session = await liveModel.connect();

// Provide a text prompt.
const prompt = "tell a short story";
session.send(prompt);

// Handle the model's audio output.
const messages = session.receive();
for await (const message of messages) {
  switch (message.type) {
    case "serverContent":
      if (message.turnComplete) {
        // TODO(developer): Handle turn completion
      } else if (message.interrupted) {
        // TODO(developer): Handle the interruption
        break;
      } else if (message.modelTurn) {
        const parts = message.modelTurn?.parts;
        parts?.forEach((part) => {
          if (part.type === "inlineData") {
            // TODO(developer): Play the audio chunk
          }
        });
      }
      break;
    case "toolCall":
      // Ignore
    case "toolCallCancellation":
      // Ignore
  }
}

Dart

如需使用 Live API，请创建 LiveGenerativeModel 实例，并将 回答模态 设置为 audio。


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

late LiveModelSession _session;

Future<Stream<Uint8List>> textToAudio(String textPrompt) async {
  WidgetsFlutterBinding.ensureInitialized();

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

  // Initialize the Gemini Developer API backend service.
  // Create a `liveGenerativeModel` instance with a model that supports the Live API.
  final liveModel = FirebaseAI.googleAI().liveGenerativeModel(
    model: 'GEMINI_LIVE_API_MODEL_NAME',
    // Configure the model to respond with audio.
    liveGenerationConfig: LiveGenerationConfig(
      responseModalities: [ResponseModalities.audio],
    ),
  );

  _session = await liveModel.connect();

  final prompt = Content.text(textPrompt);

  await _session.send(input: prompt);

  return _session.receive().asyncMap((response) async {
    if (response is LiveServerContent && response.modelTurn?.parts != null) {
       for (final part in response.modelTurn!.parts) {
         if (part is InlineDataPart) {
           return part.bytes;
         }
       }
    }
    throw Exception('Audio data not found');
  });
}

Future<void> main() async {
  try {
    final audioStream = await textToAudio('Convert this text to audio.');

    await for (final audioData in audioStream) {
      // Process the audio data (e.g., play it using an audio player package).
      print('Received audio data: ${audioData.length} bytes');
      // Example using flutter_sound (replace with your chosen package):
      // await _flutterSoundPlayer.startPlayer(fromDataBuffer: audioData);
    }
  } catch (e) {
    print('Error: $e');
  }
}

Unity

如需使用 Live API，请创建 LiveModel 实例，并将 回答模态 设置为 Audio。


using Firebase;
using Firebase.AI;

async Task SendTextReceiveAudio() {
  // Initialize the Gemini Developer API backend service.
  // Create a `LiveModel` instance with a model that supports the Live API.
  var liveModel = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetLiveModel(
    modelName: "GEMINI_LIVE_API_MODEL_NAME",
    // Configure the model to respond with audio.
    liveGenerationConfig: new LiveGenerationConfig(
        responseModalities: new[] { ResponseModality.Audio })
  );

  LiveSession session = await liveModel.ConnectAsync();

  // Provide a text prompt.
  var prompt = ModelContent.Text("Convert this text to audio.");
  await session.SendAsync(content: prompt, turnComplete: true);

  // Start receiving the response.
  await ReceiveAudio(session);
}

Queue<float> audioBuffer = new();

async Task ReceiveAudio(LiveSession session) {
  int sampleRate = 24000;
  int channelCount = 1;

  // Create a looping AudioClip to fill with the received audio data.
  int bufferSamples = (int)(sampleRate * channelCount);
  AudioClip clip = AudioClip.Create("StreamingPCM", bufferSamples, channelCount,
                                    sampleRate, true, OnAudioRead);

  // Attach the clip to an AudioSource and start playing it.
  AudioSource audioSource = GetComponent<AudioSource>();
  audioSource.clip = clip;
  audioSource.loop = true;
  audioSource.Play();

  // Start receiving the response.
  await foreach (var message in session.ReceiveAsync()) {
    // Process the received message.
    foreach (float[] pcmData in message.AudioAsFloat) {
      lock (audioBuffer) {
        foreach (float sample in pcmData) {
          audioBuffer.Enqueue(sample);
        }
      }
    }
  }
}

// This method is called by the AudioClip to load audio data.
private void OnAudioRead(float[] data) {
  int samplesToProvide = data.Length;
  int samplesProvided = 0;

  lock(audioBuffer) {
    while (samplesProvided < samplesToProvide && audioBuffer.Count > 0) {
      data[samplesProvided] = audioBuffer.Dequeue();
      samplesProvided++;
    }
  }

  while (samplesProvided < samplesToProvide) {
    data[samplesProvided] = 0.0f;
    samplesProvided++;
  }
}

请注意，您还可以在活跃会话期间将文本作为 增量内容更新 发送。

流式传输视频 + 音频输入

提供输入视频内容可为输入音频提供视觉上下文。

Live API 需要接收一系列离散的图片帧，并支持视频 帧输入，速率为 1 帧/秒 (FPS)。

  • 推荐输入：1 FPS 的原生 768x768 分辨率。

  • 支持的 MIME 类型：video/x-flv、video/quicktime、video/mpeg、 video/mpegs、video/mpg、video/mp4、video/webm、video/wmv、video/3gpp

流式传输视频 + 音频输入是一种更高级的实现，因此请查看示例应用，了解如何实现此功能： Swift - 敬请期待！| Android - 示例应用 | Web - 敬请期待！| Flutter - 示例应用 | Unity - 敬请期待！


不支持的功能

  • 使用 Live API 时， Firebase AI Logic 尚未 支持的功能， 但这些功能即将推出！

    • 配置思考 （Gemini Live 3.x 模型的默认值为 minimal）

    • 处理中断

    • 停用和配置语音活动检测 (VAD)

    • 设置输入媒体分辨率

    • 添加思考配置

    • 启用情感对话或主动音频（仅适用于 Gemini Live 2.x 模型）

    • 在回答中接收 UsageMetadata

  • 使用 Live API 时， Firebase AI Logic 不支持 的功能， 并且目前没有计划推出这些功能。

    • 服务器端提示词模板

    • 混合或设备端推理

    • Firebase 控制台中的 AI 监控


您还可以做什么？

  • 使用各种 配置选项（例如添加转写或设置回答语音）自定义实现。

  • 了解如何 管理会话，包括在会话中途更新 内容、压缩上下文窗口、检测会话何时 即将结束以及恢复会话。

  • 通过向模型授予对 工具的访问权限（例如函数调用和 Grounding with Google Search），增强实现。 即将推出有关如何将工具与 Live API 搭配使用的官方文档！

  • 了解 限制和规范， 了解使用 Live API， 例如会话时长、速率限制、支持的语言等。