Counting tokens

Methode: models.countTokens

Führt den Tokenizer eines Modells für den Eingabeinhalt aus und gibt die Tokenanzahl zurück.

Endpunkt

<ph type="x-smartling-placeholder"></ph> Beitrag https://generativelanguage.googleapis.com/v1beta/{model=models/*}:countTokens
.

Pfadparameter

model string

Erforderlich. Der Ressourcenname des Modells. Dies dient als ID für das zu verwendende Modell.

Dieser Name sollte mit einem Modellnamen übereinstimmen, der von der Methode models.list zurückgegeben wird.

Format: models/{model}. Sie hat das Format models/{model}.

Anfragetext

Der Anfragetext enthält Daten mit folgender Struktur:

<ph type="x-smartling-placeholder">
</ph> Felder
contents[] object (Content)

Optional. Die Eingabe, die dem Modell als Prompt gegeben wird. Dieses Feld wird ignoriert, wenn generateContentRequest festgelegt ist.

generateContentRequest object (GenerateContentRequest)

Optional. Die Gesamteingabe für das Modell. models.countTokens zählt Prompts, Funktionsaufrufe usw.

Beispielanfrage

Text

Python

model = genai.GenerativeModel("models/gemini-1.5-flash")

prompt = "The quick brown fox jumps over the lazy dog."

# Call `count_tokens` to get the input token count (`total_tokens`).
print("total_tokens: ", model.count_tokens(prompt))
# ( total_tokens: 10 )

response = model.generate_content(prompt)

# On the response for `generate_content`, use `usage_metadata`
# to get separate input and output token counts
# (`prompt_token_count` and `candidates_token_count`, respectively),
# as well as the combined token count (`total_token_count`).
print(response.usage_metadata)
# ( prompt_token_count: 11, candidates_token_count: 73, total_token_count: 84 )

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({
  model: "gemini-1.5-flash",
});

// Count tokens in a prompt without calling text generation.
const countResult = await model.countTokens(
  "The quick brown fox jumps over the lazy dog.",
);

console.log(countResult.totalTokens); // 11

const generateResult = await model.generateContent(
  "The quick brown fox jumps over the lazy dog.",
);

// On the response for `generateContent`, use `usageMetadata`
// to get separate input and output token counts
// (`promptTokenCount` and `candidatesTokenCount`, respectively),
// as well as the combined token count (`totalTokenCount`).
console.log(generateResult.response.usageMetadata);
// { promptTokenCount: 11, candidatesTokenCount: 131, totalTokenCount: 142 }

Muschel

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:countTokens?key=$GOOGLE_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[{
          "text": "The quick brown fox jumps over the lazy dog."
          }],
        }],
      }'

Kotlin

val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key" above)
        apiKey = BuildConfig.apiKey)

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

Swift

let generativeModel =
  GenerativeModel(
    // Specify a Gemini model appropriate for your use case
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default
  )

let prompt = "Write a story about a magic backpack."

let response = try await generativeModel.countTokens(prompt)

print("Total Tokens: \(response.totalTokens)")

Dart

final model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);
final prompt = 'The quick brown fox jumps over the lazy dog.';
final tokenCount = await model.countTokens([Content.text(prompt)]);
print('Total tokens: ${tokenCount.totalTokens}');

Java

// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // 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 inputContent =
    new Content.Builder().addText("Write a story about a magic backpack.").build();

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

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

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);

Chat

Python

model = genai.GenerativeModel("models/gemini-1.5-flash")

chat = model.start_chat(
    history=[
        {"role": "user", "parts": "Hi my name is Bob"},
        {"role": "model", "parts": "Hi Bob!"},
    ]
)
# Call `count_tokens` to get the input token count (`total_tokens`).
print(model.count_tokens(chat.history))
# ( total_tokens: 10 )

response = chat.send_message(
    "In one sentence, explain how a computer works to a young child."
)

# On the response for `send_message`, use `usage_metadata`
# to get separate input and output token counts
# (`prompt_token_count` and `candidates_token_count`, respectively),
# as well as the combined token count (`total_token_count`).
print(response.usage_metadata)
# ( prompt_token_count: 25, candidates_token_count: 21, total_token_count: 46 )

from google.generativeai.types.content_types import to_contents

# You can call `count_tokens` on the combined history and content of the next turn.
print(model.count_tokens(chat.history + to_contents("What is the meaning of life?")))
# ( total_tokens: 56 )

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({
  model: "gemini-1.5-flash",
});

const chat = model.startChat({
  history: [
    {
      role: "user",
      parts: [{ text: "Hi my name is Bob" }],
    },
    {
      role: "model",
      parts: [{ text: "Hi Bob!" }],
    },
  ],
});

const countResult = await model.countTokens({
  generateContentRequest: { contents: await chat.getHistory() },
});
console.log(countResult.totalTokens); // 10

const chatResult = await chat.sendMessage(
  "In one sentence, explain how a computer works to a young child.",
);

// On the response for `sendMessage`, use `usageMetadata`
// to get separate input and output token counts
// (`promptTokenCount` and `candidatesTokenCount`, respectively),
// as well as the combined token count (`totalTokenCount`).
console.log(chatResult.response.usageMetadata);
// { promptTokenCount: 25, candidatesTokenCount: 22, totalTokenCount: 47 }

Muschel

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:countTokens?key=$GOOGLE_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [
        {"role": "user",
        "parts": [{"text": "Hi, my name is Bob."}],
        },
        {"role": "model",
         "parts":[{"text": "Hi Bob"}],
        },
      ],
      }'

Kotlin

val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        // 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?")
                }))

val history = chat.history
val messageContent = content { text("This is the message I intend to send") }
val (totalTokens) = generativeModel.countTokens(*history.toTypedArray(), messageContent)
print(totalTokens)

Swift

let generativeModel =
  GenerativeModel(
    // Specify a Gemini model appropriate for your use case
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default
  )

// 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 = generativeModel.startChat(history: history)

let response = try await generativeModel.countTokens(chat.history + [
  ModelContent(role: "user", parts: "This is the message I intend to send"),
])
print("Total Tokens: \(response.totalTokens)")

Dart

final model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);
final chat = model.startChat(history: [
  Content.text('Hi my name is Bob'),
  Content.model([TextPart('Hi Bob!')])
]);
var tokenCount = await model.countTokens(chat.history);
print('Total tokens: ${tokenCount.totalTokens}');

final response = await chat.sendMessage(Content.text(
    'In one sentence, explain how a computer works to a young child.'));
if (response.usageMetadata case final usage?) {
  print('Prompt: ${usage.promptTokenCount}, '
      'Candidates: ${usage.candidatesTokenCount}, '
      'Total: ${usage.totalTokenCount}');
}

tokenCount = await model.countTokens(
    [...chat.history, Content.text('What is the meaning of life?')]);
print('Total tokens: ${tokenCount.totalTokens}');

Java

// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // 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);

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

Collections.addAll(history, messageContent);

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

ListenableFuture<CountTokensResponse> countTokensResponse =
    model.countTokens(history.toArray(new Content[0]));
Futures.addCallback(
    countTokensResponse,
    new FutureCallback<CountTokensResponse>() {
      @Override
      public void onSuccess(CountTokensResponse result) {
        System.out.println(result);
      }

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

Inline-Medien

Python

import PIL.Image

model = genai.GenerativeModel("models/gemini-1.5-flash")

prompt = "Tell me about this image"
your_image_file = PIL.Image.open("image.jpg")

# Call `count_tokens` to get the input token count
# of the combined text and file (`total_tokens`).
# An image's display or file size does not affect its token count.
# Optionally, you can call `count_tokens` for the text and file separately.
print(model.count_tokens([prompt, your_image_file]))
# ( total_tokens: 263 )

response = model.generate_content([prompt, your_image_file])

# On the response for `generate_content`, use `usage_metadata`
# to get separate input and output token counts
# (`prompt_token_count` and `candidates_token_count`, respectively),
# as well as the combined token count (`total_token_count`).
print(response.usage_metadata)
# ( prompt_token_count: 264, candidates_token_count: 80, total_token_count: 345 )

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({
  model: "gemini-1.5-flash",
});

function fileToGenerativePart(path, mimeType) {
  return {
    inlineData: {
      data: Buffer.from(fs.readFileSync(path)).toString("base64"),
      mimeType,
    },
  };
}

const imagePart = fileToGenerativePart(
  `${mediaPath}/jetpack.jpg`,
  "image/jpeg",
);

const prompt = "Tell me about this image.";

// Call `countTokens` to get the input token count
// of the combined text and file (`totalTokens`).
// An image's display or file size does not affect its token count.
// Optionally, you can call `countTokens` for the text and file separately.
const countResult = await model.countTokens([prompt, imagePart]);
console.log(countResult.totalTokens); // 265

const generateResult = await model.generateContent([prompt, imagePart]);

// On the response for `generateContent`, use `usageMetadata`
// to get separate input and output token counts
// (`promptTokenCount` and `candidatesTokenCount`, respectively),
// as well as the combined token count (`totalTokenCount`).
console.log(generateResult.response.usageMetadata);
// { promptTokenCount: 265, candidatesTokenCount: 157, totalTokenCount: 422 }

Kotlin

val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key" above)
        apiKey = BuildConfig.apiKey)

val image1: Bitmap = BitmapFactory.decodeResource(context.resources, R.drawable.image1)
val image2: Bitmap = BitmapFactory.decodeResource(context.resources, R.drawable.image2)

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

val (totalTokens) = generativeModel.countTokens(multiModalContent)
print(totalTokens)

Swift

let generativeModel =
  GenerativeModel(
    // Specify a Gemini model appropriate for your use case
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default
  )

guard let image1 = UIImage(systemName: "cloud.sun") else { fatalError() }
guard let image2 = UIImage(systemName: "cloud.heavyrain") else { fatalError() }

let prompt = "What's the difference between these pictures?"

let response = try await generativeModel.countTokens(image1, image2, prompt)
print("Total Tokens: \(response.totalTokens)")

Dart

final model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);

Future<DataPart> fileToPart(String mimeType, String path) async {
  return DataPart(mimeType, await File(path).readAsBytes());
}

final prompt = 'Tell me about this image';
final image = await fileToPart('image/jpeg', 'resources/organ.jpg');
final content = Content.multi([TextPart(prompt), image]);

// An image's display size does not affet its token count.
// Optionally, you can call `countTokens` for the prompt and file separately.
final tokenCount = await model.countTokens([content]);
print('Total tokens: ${tokenCount.totalTokens}');

final response = await model.generateContent([content]);
if (response.usageMetadata case final usage?) {
  print('Prompt: ${usage.promptTokenCount}, '
      'Candidates: ${usage.candidatesTokenCount}, '
      'Total: ${usage.totalTokenCount}');
}

Java

// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // 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 text = new Content.Builder().addText("Write a story about a magic backpack.").build();

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

// For text-and-image input
Bitmap image1 = BitmapFactory.decodeResource(context.getResources(), R.drawable.image1);
Bitmap image2 = BitmapFactory.decodeResource(context.getResources(), R.drawable.image2);

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

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

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);

Dateien

Python

import time

model = genai.GenerativeModel("models/gemini-1.5-flash")

prompt = "Tell me about this video"
your_file = genai.upload_file(path=media / "Big_Buck_Bunny.mp4")

# Videos need to be processed before you can use them.
while your_file.state.name == "PROCESSING":
    print("processing video...")
    time.sleep(5)
    your_file = genai.get_file(your_file.name)

# Call `count_tokens` to get the input token count
# of the combined text and video/audio file (`total_tokens`).
# A video or audio file is converted to tokens at a fixed rate of tokens per second.
# Optionally, you can call `count_tokens` for the text and file separately.
print(model.count_tokens([prompt, your_file]))
# ( total_tokens: 300 )

response = model.generate_content([prompt, your_file])

# On the response for `generate_content`, use `usage_metadata`
# to get separate input and output token counts
# (`prompt_token_count` and `candidates_token_count`, respectively),
# as well as the combined token count (`total_token_count`).
print(response.usage_metadata)
# ( prompt_token_count: 301, candidates_token_count: 60, total_token_count: 361 )

Node.js

// Make sure to include these imports:
// import { GoogleAIFileManager, FileState } from "@google/generative-ai/server";
// import { GoogleGenerativeAI } from "@google/generative-ai";
const fileManager = new GoogleAIFileManager(process.env.API_KEY);

const uploadVideoResult = await fileManager.uploadFile(
  `${mediaPath}/Big_Buck_Bunny.mp4`,
  { mimeType: "video/mp4" },
);

let file = await fileManager.getFile(uploadVideoResult.file.name);
process.stdout.write("processing video");
while (file.state === FileState.PROCESSING) {
  process.stdout.write(".");
  // Sleep for 10 seconds
  await new Promise((resolve) => setTimeout(resolve, 10_000));
  // Fetch the file from the API again
  file = await fileManager.getFile(uploadVideoResult.file.name);
}

if (file.state === FileState.FAILED) {
  throw new Error("Video processing failed.");
} else {
  process.stdout.write("\n");
}

const videoPart = {
  fileData: {
    fileUri: uploadVideoResult.file.uri,
    mimeType: uploadVideoResult.file.mimeType,
  },
};

const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({
  model: "gemini-1.5-flash",
});

const prompt = "Tell me about this video.";

// Call `countTokens` to get the input token count
// of the combined text and file (`totalTokens`).
// An video or audio file's display or file size does not affect its token count.
// Optionally, you can call `countTokens` for the text and file separately.
const countResult = await model.countTokens([prompt, videoPart]);

console.log(countResult.totalTokens); // 302

const generateResult = await model.generateContent([prompt, videoPart]);

// On the response for `generateContent`, use `usageMetadata`
// to get separate input and output token counts
// (`promptTokenCount` and `candidatesTokenCount`, respectively),
// as well as the combined token count (`totalTokenCount`).
console.log(generateResult.response.usageMetadata);
// { promptTokenCount: 302, candidatesTokenCount: 46, totalTokenCount: 348 }

Cache

Python

import time

model = genai.GenerativeModel("models/gemini-1.5-flash")

your_file = genai.upload_file(path=media / "a11.txt")

cache = genai.caching.CachedContent.create(
    model="models/gemini-1.5-flash-001",
    # You can set the system_instruction and tools
    system_instruction=None,
    tools=None,
    contents=["Here the Apollo 11 transcript:", your_file],
)

model = genai.GenerativeModel.from_cached_content(cache)

prompt = "Please give a short summary of this file."

# Call `count_tokens` to get input token count
# of the combined text and file (`total_tokens`).
# A video or audio file is converted to tokens at a fixed rate of tokens per second.
# Optionally, you can call `count_tokens` for the text and file separately.
print(model.count_tokens(prompt))
# ( total_tokens: 9 )

response = model.generate_content(prompt)

# On the response for `generate_content`, use `usage_metadata`
# to get separate input and output token counts
# (`prompt_token_count` and `candidates_token_count`, respectively),
# as well as the cached content token count and the combined total token count.
print(response.usage_metadata)
# ( prompt_token_count: 323393, cached_content_token_count: 323383, candidates_token_count: 64)
# ( total_token_count: 323457 )

cache.delete()

Node.js

// Make sure to include these imports:
// import { GoogleAIFileManager, GoogleAICacheManager } from "@google/generative-ai/server";
// import { GoogleGenerativeAI } from "@google/generative-ai";

// Upload large text file.
const fileManager = new GoogleAIFileManager(process.env.API_KEY);
const uploadResult = await fileManager.uploadFile(`${mediaPath}/a11.txt`, {
  mimeType: "text/plain",
});

// Create a cache that uses the uploaded file.
const cacheManager = new GoogleAICacheManager(process.env.API_KEY);
const cacheResult = await cacheManager.create({
  ttlSeconds: 600,
  model: "models/gemini-1.5-flash-001",
  contents: [
    {
      role: "user",
      parts: [{ text: "Here's the Apollo 11 transcript:" }],
    },
    {
      role: "user",
      parts: [
        {
          fileData: {
            fileUri: uploadResult.file.uri,
            mimeType: uploadResult.file.mimeType,
          },
        },
      ],
    },
  ],
});

const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModelFromCachedContent(cacheResult);

const prompt = "Please give a short summary of this file.";

// Call `countTokens` to get the input token count
// of the combined text and file (`totalTokens`).
const result = await model.countTokens(prompt);

console.log(result.totalTokens); // 10

const generateResult = await model.generateContent(prompt);

// On the response for `generateContent`, use `usageMetadata`
// to get separate input and output token counts
// (`promptTokenCount` and `candidatesTokenCount`, respectively),
// as well as the cached content token count and the combined total
// token count.
console.log(generateResult.response.usageMetadata);
// {
//   promptTokenCount: 323396,
//   candidatesTokenCount: 113,
//   totalTokenCount: 323509,
//   cachedContentTokenCount: 323386
// }

await cacheManager.delete(cacheResult.name);

Systemanweisung

Python

model = genai.GenerativeModel(model_name="gemini-1.5-flash")

prompt = "The quick brown fox jumps over the lazy dog."

print(model.count_tokens(prompt))
# total_tokens: 10

model = genai.GenerativeModel(
    model_name="gemini-1.5-flash", system_instruction="You are a cat. Your name is Neko."
)

# The total token count includes everything sent to the `generate_content` request.
# When you use system instructions, the total token count increases.
print(model.count_tokens(prompt))
# ( total_tokens: 21 )

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({
  model: "models/gemini-1.5-flash",
  systemInstruction: "You are a cat. Your name is Neko.",
});

const result = await model.countTokens(
  "The quick brown fox jumps over the lazy dog.",
);

console.log(result);
// {
//   totalTokens: 23,
//   systemInstructionsTokens: { partTokens: [ 11 ], roleTokens: 1 },
//   contentTokens: [ { partTokens: [Array], roleTokens: 1 } ]
// }

Kotlin

val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key" above)
        apiKey = BuildConfig.apiKey,
        systemInstruction = content(role = "system") { text("You are a cat. Your name is Neko.")}
    )

// For text-only input
val (totalTokens) = generativeModel.countTokens("What is your name?")
print(totalTokens)

Swift

let generativeModel =
  GenerativeModel(
    // Specify a model that supports system instructions, like a Gemini 1.5 model
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default,
    systemInstruction: ModelContent(role: "system", parts: "You are a cat. Your name is Neko.")
  )

let prompt = "What is your name?"

let response = try await generativeModel.countTokens(prompt)
print("Total Tokens: \(response.totalTokens)")

Dart

var model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);
final prompt = 'The quick brown fox jumps over the lazy dog.';

// The total token count includes everything sent in the `generateContent`
// request.
var tokenCount = await model.countTokens([Content.text(prompt)]);
print('Total tokens: ${tokenCount.totalTokens}');
model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
  systemInstruction: Content.system('You are a cat. Your name is Neko.'),
);
tokenCount = await model.countTokens([Content.text(prompt)]);
print('Total tokens: ${tokenCount.totalTokens}');

Java

// Create your system instructions
Content systemInstruction =
    new Content.Builder().addText("You are a cat. Your name is Neko.").build();

// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key"
        // above)
        /* apiKey */ BuildConfig.apiKey,
        /* generationConfig (optional) */ null,
        /* safetySettings (optional) */ null,
        /* requestOptions (optional) */ new RequestOptions(),
        /* tools (optional) */ null,
        /* toolsConfig (optional) */ null,
        /* systemInstruction (optional) */ systemInstruction);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Content inputContent = new Content.Builder().addText("What's your name?.").build();

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

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

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);

Tools

Python

model = genai.GenerativeModel(model_name="gemini-1.5-flash")

prompt = "I have 57 cats, each owns 44 mittens, how many mittens is that in total?"

print(model.count_tokens(prompt))
# ( total_tokens: 22 )

def add(a: float, b: float):
    """returns a + b."""
    return a + b

def subtract(a: float, b: float):
    """returns a - b."""
    return a - b

def multiply(a: float, b: float):
    """returns a * b."""
    return a * b

def divide(a: float, b: float):
    """returns a / b."""
    return a / b

model = genai.GenerativeModel(
    "models/gemini-1.5-flash-001", tools=[add, subtract, multiply, divide]
)

# The total token count includes everything sent to the `generate_content` request.
# When you use tools (like function calling), the total token count increases.
print(model.count_tokens(prompt))
# ( total_tokens: 206 )

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);

const functionDeclarations = [
  { name: "add" },
  { name: "subtract" },
  { name: "multiply" },
  { name: "divide" },
];

const model = genAI.getGenerativeModel({
  model: "models/gemini-1.5-flash",
  tools: [{ functionDeclarations }],
});

const result = await model.countTokens(
  "I have 57 cats, each owns 44 mittens, how many mittens is that in total?",
);

console.log(result);
// {
//   totalTokens: 99,
//   systemInstructionsTokens: {},
//   contentTokens: [ { partTokens: [Array], roleTokens: 1 } ],
//   toolTokens: [ { functionDeclarationTokens: [Array] } ]
// }

Kotlin

val multiplyDefinition = defineFunction(
    name = "multiply",
    description = "returns the product of the provided numbers.",
    parameters = listOf(
        Schema.double("a", "First number"),
        Schema.double("b", "Second number")
    )
)
val usableFunctions = listOf(multiplyDefinition)

val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key" above)
        apiKey = BuildConfig.apiKey,
        tools = listOf(Tool(usableFunctions))
    )

// For text-only input
val (totalTokens) = generativeModel.countTokens("What's the product of 9 and 358?")
print(totalTokens)

Swift

let generativeModel =
  GenerativeModel(
    // Specify a model that supports system instructions, like a Gemini 1.5 model
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default,
    tools: [Tool(functionDeclarations: [
      FunctionDeclaration(
        name: "controlLight",
        description: "Set the brightness and color temperature of a room light.",
        parameters: [
          "brightness": Schema(
            type: .number,
            format: "double",
            description: "Light level from 0 to 100. Zero is off and 100 is full brightness."
          ),
          "colorTemperature": Schema(
            type: .string,
            format: "enum",
            description: "Color temperature of the light fixture.",
            enumValues: ["daylight", "cool", "warm"]
          ),
        ],
        requiredParameters: ["brightness", "colorTemperature"]
      ),
    ])]
  )

let prompt = "Dim the lights so the room feels cozy and warm."

let response = try await generativeModel.countTokens(prompt)
print("Total Tokens: \(response.totalTokens)")

Dart

var model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);
final prompt = 'I have 57 cats, each owns 44 mittens, '
    'how many mittens is that in total?';

// The total token count includes everything sent in the `generateContent`
// request.
var tokenCount = await model.countTokens([Content.text(prompt)]);
print('Total tokens: ${tokenCount.totalTokens}');
final binaryFunction = Schema.object(
  properties: {
    'a': Schema.number(nullable: false),
    'b': Schema.number(nullable: false)
  },
  requiredProperties: ['a', 'b'],
);

model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
  tools: [
    Tool(functionDeclarations: [
      FunctionDeclaration('add', 'returns a + b', binaryFunction),
      FunctionDeclaration('subtract', 'returns a - b', binaryFunction),
      FunctionDeclaration('multipley', 'returns a * b', binaryFunction),
      FunctionDeclaration('divide', 'returns a / b', binaryFunction)
    ])
  ],
);
tokenCount = await model.countTokens([Content.text(prompt)]);
print('Total tokens: ${tokenCount.totalTokens}');

Java

FunctionDeclaration multiplyDefinition =
    defineFunction(
        /* name  */ "multiply",
        /* description */ "returns a * b.",
        /* parameters */ Arrays.asList(
            Schema.numDouble("a", "First parameter"),
            Schema.numDouble("b", "Second parameter")),
        /* required */ Arrays.asList("a", "b"));

Tool tool = new Tool(Arrays.asList(multiplyDefinition), null);
;

// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key"
        // above)
        /* apiKey */ BuildConfig.apiKey,
        /* generationConfig (optional) */ null,
        /* safetySettings (optional) */ null,
        /* requestOptions (optional) */ new RequestOptions(),
        /* tools (optional) */ Arrays.asList(tool));
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Content inputContent = new Content.Builder().addText("What's your name?.").build();

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

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

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);

Antworttext

Eine Antwort von models.countTokens.

Sie gibt den tokenCount des Modells für prompt zurück.

Bei Erfolg enthält der Antworttext Daten mit der folgenden Struktur:

Felder
totalTokens integer

Die Anzahl der Tokens, in die der model die prompt tokenisiert.

Immer positiv. Wenn „cacheContent“ festgelegt ist, ist dies immer noch die effektive Gesamtgröße des Prompts. Das heißt: Dazu gehört auch die Anzahl der Tokens im Cache-Inhalt.

JSON-Darstellung 
{
  "totalTokens": integer
}

GenerateContentRequest

Anfrage zum Generieren einer Vervollständigung aus dem Modell.

JSON-Darstellung 
{
  "model": string,
  "contents": [
    {
      object (Content)
    }
  ],
  "tools": [
    {
      object (Tool)
    }
  ],
  "toolConfig": {
    object (ToolConfig)
  },
  "safetySettings": [
    {
      object (SafetySetting)
    }
  ],
  "systemInstruction": {
    object (Content)
  },
  "generationConfig": {
    object (GenerationConfig)
  },
  "cachedContent": string
}
Felder
model string

Erforderlich. Der Name des Model, der zum Generieren der Vervollständigung verwendet werden soll.

Format: name=models/{model}.

contents[] object (Content)

Erforderlich. Der Inhalt der aktuellen Unterhaltung mit dem Modell.

Bei Einzelabfragen ist dies eine einzelne Instanz. Bei Abfragen mit mehreren Antworten ist dies ein wiederkehrendes Feld, das den Unterhaltungsverlauf und die letzte Anfrage enthält.

tools[] object (Tool)

Optional. Eine Liste von Tools, die das Modell verwenden kann, um die nächste Antwort zu generieren.

Eine Tool ist ein Code-Snippet, das es dem System ermöglicht, mit externen Systemen zu interagieren, um eine Aktion oder eine Reihe von Aktionen auszuführen, ohne das Wissen und den Umfang des Modells zu überschreiten. Das einzige unterstützte Tool ist derzeit Function.

toolConfig object (ToolConfig)

Optional. Toolkonfiguration für eine in der Anfrage angegebene Tool.

safetySettings[] object (SafetySetting)

Optional. Eine Liste einzelner SafetySetting-Instanzen zum Blockieren unsicherer Inhalte.

Dies wird am GenerateContentRequest.contents und GenerateContentResponse.candidates erzwungen. Es darf nicht mehr als eine Einstellung für jeden SafetyCategory-Typ vorhanden sein. Die API blockiert alle Inhalte und Antworten, die die in diesen Einstellungen festgelegten Grenzwerte nicht erreichen. Diese Liste überschreibt die Standardeinstellungen für jeden in „safetySettings“ festgelegten SafetyCategory. Wenn für eine bestimmte SafetyCategory in der Liste keine SafetySetting angegeben ist, verwendet die API die standardmäßige Sicherheitseinstellung für diese Kategorie. Die schädlichen Kategorien HARM_CATEGORY_HATE_SPEECH, HARM_CATEGORY_SEXUALLY_EXPLICIT, HARM_CATEGORY_DANGEROUS_CONTENT und HARM_CATEGORY_HARASSMENT werden unterstützt.

systemInstruction object (Content)

Optional. Systemanweisung für Entwicklersatz. Derzeit nur Text.

generationConfig object (GenerationConfig)

Optional. Konfigurationsoptionen für Modellgenerierung und -ausgaben.

cachedContent string

Optional. Der Name des im Cache gespeicherten Inhalts, der als Kontext für die Vorhersage verwendet wird. Hinweis: Wird nur beim expliziten Caching verwendet, bei dem die Nutzer die Kontrolle über das Caching haben (z.B. welche Inhalte im Cache gespeichert werden sollen) und bei denen die Kosten eingespart werden können. Format: cachedContents/{cachedContent}