Bắt đầu sử dụng API Gemini trong ứng dụng Android (SDK ứng dụng)

Kotlin

// Access your API key as a Build Configuration variable
val apiKey = BuildConfig.apiKey

Java

// Access your API key as a Build Configuration variable
String apiKey = BuildConfig.apiKey;

Kotlin

val generativeModel = GenerativeModel(
    // Use a model that's applicable for your use case (see "Implement basic use cases" below)
    modelName = "MODEL_NAME",
    // Access your API key as a Build Configuration variable (see "Set up your API key" above)
    apiKey = BuildConfig.apiKey
)

Java

Đối với Java, bạn cũng cần khởi chạy đối tượng GenerativeModelFutures.

// Use a model that's applicable for your use case (see "Implement basic use cases" below)
GenerativeModel gm = new GenerativeModel(/* modelName */ "MODEL_NAME",
// Access your API key as a Build Configuration variable (see "Set up your API key" above)
    /* apiKey */ BuildConfig.apiKey);

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Kotlin

Lưu ý rằng generateContent() là một hàm tạm ngưng và cần được gọi từ phạm vi Coroutine. Nếu bạn chưa hiểu rõ về Coroutine, hãy đọc bài viết Coroutine Kotlin trên Android.

val generativeModel = GenerativeModel(
    // For text-only input, use the gemini-pro model
    modelName = "gemini-pro",
    // Access your API key as a Build Configuration variable (see "Set up your API key" above)
    apiKey = BuildConfig.apiKey
)

val prompt = "Write a story about a magic backpack."
val response = generativeModel.generateContent(prompt)
print(response.text)

Java

Xin lưu ý rằng generateContent() trả về ListenableFuture. Nếu bạn chưa hiểu rõ về API này, hãy xem tài liệu của Android về cách Sử dụng ListenableFuture.

// For text-only input, use the gemini-pro model
GenerativeModel gm = new GenerativeModel(/* modelName */ "gemini-pro",
// Access your API key as a Build Configuration variable (see "Set up your API key" above)
    /* apiKey */ BuildConfig.apiKey);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Content content = new Content.Builder()
    .addText("Write a story about a magic backpack.")
    .build();

Executor executor = // ...

ListenableFuture<GenerateContentResponse> response = model.generateContent(content);
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);

Kotlin

Lưu ý rằng generateContent() là một hàm tạm ngưng và cần được gọi từ phạm vi Coroutine. Nếu bạn chưa hiểu rõ về Coroutine, hãy đọc bài viết Coroutine Kotlin trên Android.

val generativeModel = GenerativeModel(
    // For text-and-images input (multimodal), use the gemini-pro-vision model
    modelName = "gemini-pro-vision",
    // Access your API key as a Build Configuration variable (see "Set up your API key" above)
    apiKey = BuildConfig.apiKey
)

val image1: Bitmap = // ...
val image2: Bitmap = // ...

val inputContent = content {
    image(image1)
    image(image2)
    text("What's different between these pictures?")
}

val response = generativeModel.generateContent(inputContent)
print(response.text)

Java

Xin lưu ý rằng generateContent() trả về ListenableFuture. Nếu bạn chưa hiểu rõ về API này, hãy xem tài liệu của Android về cách Sử dụng ListenableFuture.

// For text-and-images input (multimodal), use the gemini-pro-vision model
GenerativeModel gm = new GenerativeModel(/* modelName */ "gemini-pro-vision",
// Access your API key as a Build Configuration variable (see "Set up your API key" above)
    /* apiKey */ BuildConfig.apiKey);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Bitmap image1 = // ...
Bitmap image2 = // ...

Content content = new Content.Builder()
    .addText("What's different between these pictures?")
    .addImage(image1)
    .addImage(image2)
    .build();

Executor executor = // ...

ListenableFuture<GenerateContentResponse> response = model.generateContent(content);
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);

Kotlin

Lưu ý rằng generateContent() là một hàm tạm ngưng và cần được gọi từ phạm vi Coroutine. Nếu bạn chưa hiểu rõ về Coroutine, hãy đọc bài viết Coroutine Kotlin trên Android.

val generativeModel = GenerativeModel(
    // For text-only input, use the gemini-pro model
    modelName = "gemini-pro",
    // Access your API key as a Build Configuration variable (see "Set up your API key" above)
    apiKey = BuildConfig.apiKey
)

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.sendMessage("How many paws are in my house?")

Java

Xin lưu ý rằng generateContent() trả về ListenableFuture. Nếu bạn chưa hiểu rõ về API này, hãy xem tài liệu của Android về cách Sử dụng ListenableFuture.

// For text-only input, use the gemini-pro model
GenerativeModel gm = new GenerativeModel(/* modelName */ "gemini-pro",
// Access your API key as a Build Configuration variable (see "Set up your API key" above)
    /* apiKey */ BuildConfig.apiKey);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

// (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 userMessage = new Content.Builder()
    .setRole("user")
    .addText("How many paws are in my house?")
    .build();

Executor executor = // ...

// Send the message
ListenableFuture<GenerateContentResponse> response = chat.sendMessage(userMessage);

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);

Kotlin

Lưu ý rằng generateContentStream() là một hàm tạm ngưng và cần được gọi từ phạm vi Coroutine. Nếu bạn chưa hiểu rõ về Coroutine, hãy đọc bài viết Coroutine Kotlin trên Android.

val generativeModel = GenerativeModel(
    // For text-and-image input (multimodal), use the gemini-pro-vision model
    modelName = "gemini-pro-vision",
    // Access your API key as a Build Configuration variable (see "Set up your API key" above)
    apiKey = BuildConfig.apiKey
)

val image1: Bitmap = // ...
val image2: Bitmap = // ...

val inputContent = content {
    image(image1)
    image(image2)
    text("What's the difference between these pictures?")
}

var fullResponse = ""
generativeModel.generateContentStream(inputContent).collect { chunk ->
    print(chunk.text)
    fullResponse += chunk.text
}

Java

Các phương thức truyền trực tuyến Java trong SDK này sẽ trả về loại Publisher từ thư viện Reactive khoẻ (Luồng phản ứng).

// For text-and-images input (multimodal), use the gemini-pro-vision model
GenerativeModel gm = new GenerativeModel(/* modelName */ "gemini-pro-vision",
// Access your API key as a Build Configuration variable (see "Set up your API key" above)
    /* apiKey */ BuildConfig.apiKey);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Bitmap image1 = // ...
Bitmap image2 = // ...

Content content = new Content.Builder()
    .addText("What's different between these pictures?")
    .addImage(image1)
    .addImage(image2)
    .build();

Publisher<GenerateContentResponse> streamingResponse =
    model.generateContentStream(content);

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]);
    }

    @Override
    public void onError(Throwable t) {
        t.printStackTrace();
    }

    @Override
    public void onSubscribe(Subscription s) { }
});

Kotlin

Lưu ý rằng generateContentStream() là một hàm tạm ngưng và cần được gọi từ phạm vi Coroutine. Nếu bạn chưa hiểu rõ về Coroutine, hãy đọc bài viết Coroutine Kotlin trên Android.

// Use streaming with text-only input
generativeModel.generateContentStream(inputContent).collect { chunk ->
    print(chunk.text)
}

// Use streaming with multi-turn conversations (like chat)
val chat = generativeModel.startChat()
chat.sendMessageStream(inputContent).collect { chunk ->
    print(chunk.text)
}

Java

Các phương thức truyền trực tuyến Java trong SDK này sẽ trả về loại Publisher từ thư viện Reactive khoẻ (Luồng phản ứng).

// Use streaming with text-only input
Publisher<GenerateContentResponse> streamingResponse =
    model.generateContentStream(inputContent);

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
});

// Use streaming with multi-turn conversations (like chat)
ChatFutures chat = model.startChat(history);

Publisher<GenerateContentResponse> streamingResponse =
    chat.sendMessageStream(inputContent);

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
});

Kotlin

Lưu ý rằng countTokens() là một hàm tạm ngưng và cần được gọi từ phạm vi Coroutine. Nếu bạn chưa hiểu rõ về Coroutine, hãy đọc bài viết Coroutine Kotlin trên Android.

// For text-only input
val (totalTokens) = generativeModel.countTokens("Write a story about a magic backpack.")

// For text-and-image input (multi-modal)
val multiModalContent = content {
    image(image1)
    image(image2)
    text("What's the difference between these pictures?")
}

val (totalTokens) = generativeModel.countTokens(multiModalContent)

// For multi-turn conversations (like chat)
val history = chat.history
val messageContent = content { text("This is the message I intend to send")}
val (totalTokens) = generativeModel.countTokens(*history.toTypedArray(), messageContent)

Java

Xin lưu ý rằng countTokens() trả về ListenableFuture. Nếu bạn chưa hiểu rõ về API này, hãy xem tài liệu của Android về cách Sử dụng ListenableFuture.

Content text = new Content.Builder()
    .addText("Write a story about a magic backpack.")
    .build();

Executor executor = // ...

// For text-only input
ListenableFuture<CountTokensResponse> countTokensResponse = model.countTokens(text);

Futures.addCallback(countTokensResponse, new FutureCallback<CountTokensResponse>() {
    @Override
    public void onSuccess(CountTokensResponse result) {
        int totalTokens = result.getTotalTokens();
        System.out.println("TotalTokens = " + totalTokens);
    }

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

// For text-and-image input
Bitmap image1 = // ...
Bitmap image2 = // ...

Content multiModalContent = new Content.Builder()
    .addImage(image1)
    .addImage(image2)
    .addText("What's different between these pictures?")
    .build();

ListenableFuture<CountTokensResponse> countTokensResponse = model.countTokens(multiModalContent);

// For multi-turn conversations (like chat)
List<Content> history = chat.getChat().getHistory();

Content messageContent = new Content.Builder()
    .addText("This is the message I intend to send")
    .build();

Collections.addAll(history, messageContent);

ListenableFuture<CountTokensResponse> countTokensResponse = model.countTokens(history.toArray(new Content[0]));

Kotlin

val config = generationConfig {
    temperature = 0.9f
    topK = 16
    topP = 0.1f
    maxOutputTokens = 200
    stopSequences = listOf("red")
}

val generativeModel = GenerativeModel(
    modelName = "MODEL_NAME",
    apiKey = BuildConfig.apiKey,
    generationConfig = config
)

Java

GenerationConfig.Builder configBuilder = new GenerationConfig.Builder();
configBuilder.temperature = 0.9f;
configBuilder.topK = 16;
configBuilder.topP = 0.1f;
configBuilder.maxOutputTokens = 200;
configBuilder.stopSequences = Arrays.asList("red");

GenerationConfig generationConfig = configBuilder.build();

GenerativeModel gm = new GenerativeModel(
    "MODEL_NAME",
    BuildConfig.apiKey,
    generationConfig
);

GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Kotlin

val generativeModel = GenerativeModel(
    modelName = "MODEL_NAME",
    apiKey = BuildConfig.apiKey,
    safetySettings = listOf(
        SafetySetting(HarmCategory.HARASSMENT, BlockThreshold.ONLY_HIGH)
    )
)

Java

SafetySetting harassmentSafety = new SafetySetting(HarmCategory.HARASSMENT,
    BlockThreshold.ONLY_HIGH);

GenerativeModel gm = new GenerativeModel(
    "MODEL_NAME",
    BuildConfig.apiKey,
    null, // generation config is optional
    Collections.singletonList(harassmentSafety)
);

GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Kotlin

val harassmentSafety = SafetySetting(HarmCategory.HARASSMENT, BlockThreshold.ONLY_HIGH)

val hateSpeechSafety = SafetySetting(HarmCategory.HATE_SPEECH, BlockThreshold.MEDIUM_AND_ABOVE)

val generativeModel = GenerativeModel(
    modelName = "MODEL_NAME",
    apiKey = BuildConfig.apiKey,
    safetySettings = listOf(harassmentSafety, hateSpeechSafety)
)

Java

SafetySetting harassmentSafety = new SafetySetting(HarmCategory.HARASSMENT,
    BlockThreshold.ONLY_HIGH);

SafetySetting hateSpeechSafety = new SafetySetting(HarmCategory.HATE_SPEECH,
    BlockThreshold.MEDIUM_AND_ABOVE);

GenerativeModel gm = new GenerativeModel(
    "MODEL_NAME",
    BuildConfig.apiKey,
    null, // generation config is optional
    Arrays.asList(harassmentSafety, hateSpeechSafety)
);

GenerativeModelFutures model = GenerativeModelFutures.from(gm);