Counting tokens

מדריך מפורט לספירת אסימונים באמצעות Gemini API, כולל הסבר על אופן הספירה של תמונות, אודיו וסרטונים, זמין במדריך לספירת אסימונים ובמתכון במדריך למתכונים.

שיטה: models.countTokens

הפונקציה מפעילה את ה-tokenizer של המודל על הקלט Content ומחזירה את מספר האסימונים. מידע נוסף על אסימונים זמין במדריך על אסימונים.

נקודת קצה

פוסט https://generativelanguage.googleapis.com/v1beta/{model=models/*}:countTokens

פרמטרים של נתיב

model string

חובה. שם המשאב של הדגם. זהו מזהה לשימוש במודל.

השם הזה צריך להתאים לשם הדגם שהוחזר על ידי השיטה models.list.

פורמט: models/{model} הפורמט הוא models/{model}.

גוף הבקשה

גוף הבקשה מכיל נתונים במבנה הבא:

שדות
contents[] object (Content)

זה שינוי אופציונלי. הקלט שסופק למודל כהנחיה. המערכת מתעלמת מהשדה הזה כשהשדה generateContentRequest מוגדר.

generateContentRequest object (GenerateContentRequest)

זה שינוי אופציונלי. הקלט הכולל שניתן ל-Model. זה כולל את ההנחיה וגם מידע נוסף לגבי הכיוון של המודל, כמו הוראות מערכת ו/או הצהרות על פונקציות לקריאה לפונקציה. Model/Content ו-generateContentRequest הם ערכים בלעדיים. אפשר לשלוח Model + Content או generateContentRequest, אבל לא את שניהם.

בקשה לדוגמה

טקסטצ'אטמדיה מוטמעתוידאוPDFמטמוןהוראות למערכתכלים
PythonNode.jsGoקונכייהKotlinSwiftDartJava
from google import genai

client = genai.Client()
prompt = "The quick brown fox jumps over the lazy dog."

# Count tokens using the new client method.
total_tokens = client.models.count_tokens(
    model="gemini-2.0-flash", contents=prompt
)
print("total_tokens: ", total_tokens)
# ( e.g., total_tokens: 10 )

response = client.models.generate_content(
    model="gemini-2.0-flash", contents=prompt
)

# The usage_metadata provides detailed token counts.
print(response.usage_metadata)
# ( e.g., prompt_token_count: 11, candidates_token_count: 73, total_token_count: 84 )
count_tokens.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const prompt = "The quick brown fox jumps over the lazy dog.";
const countTokensResponse = await ai.models.countTokens({
  model: "gemini-2.0-flash",
  contents: prompt,
});
console.log(countTokensResponse.totalTokens);

const generateResponse = await ai.models.generateContent({
  model: "gemini-2.0-flash",
  contents: prompt,
});
console.log(generateResponse.usageMetadata);
count_tokens.js
 
model := client.GenerativeModel("gemini-1.5-flash")
prompt := "The quick brown fox jumps over the lazy dog"

// Call CountTokens to get the input token count (`total tokens`).
tokResp, err := model.CountTokens(ctx, genai.Text(prompt))
if err != nil {
	log.Fatal(err)
}

fmt.Println("total_tokens:", tokResp.TotalTokens)
// ( total_tokens: 10 )

resp, err := model.GenerateContent(ctx, genai.Text(prompt))
if err != nil {
	log.Fatal(err)
}

// 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).
fmt.Println("prompt_token_count:", resp.UsageMetadata.PromptTokenCount)
fmt.Println("candidates_token_count:", resp.UsageMetadata.CandidatesTokenCount)
fmt.Println("total_token_count:", resp.UsageMetadata.TotalTokenCount)
// ( prompt_token_count: 10, candidates_token_count: 38, total_token_count: 48 )
docs-snippets_test.go
 
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:countTokens?key=$GEMINI_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[{
          "text": "The quick brown fox jumps over the lazy dog."
          }],
        }],
      }'
count_tokens.sh
 
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)
count_tokens.kt
 
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)")
CountTokens.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.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}');
count_tokens.dart
 
// 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);
count_tokens.java
PythonNode.jsGoקונכייהKotlinSwiftDartJava
from google import genai
from google.genai import types

client = genai.Client()

chat = client.chats.create(
    model="gemini-2.0-flash",
    history=[
        types.Content(
            role="user", parts=[types.Part(text="Hi my name is Bob")]
        ),
        types.Content(role="model", parts=[types.Part(text="Hi Bob!")]),
    ],
)
# Count tokens for the chat history.
print(
    client.models.count_tokens(
        model="gemini-2.0-flash", contents=chat.get_history()
    )
)
# ( e.g., total_tokens: 10 )

response = chat.send_message(
    message="In one sentence, explain how a computer works to a young child."
)
print(response.usage_metadata)
# ( e.g., prompt_token_count: 25, candidates_token_count: 21, total_token_count: 46 )

# You can count tokens for the combined history and a new message.
extra = types.UserContent(
    parts=[
        types.Part(
            text="What is the meaning of life?",
        )
    ]
)
history = chat.get_history()
history.append(extra)
print(client.models.count_tokens(model="gemini-2.0-flash", contents=history))
# ( e.g., total_tokens: 56 )
count_tokens.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
// Initial chat history.
const history = [
  { role: "user", parts: [{ text: "Hi my name is Bob" }] },
  { role: "model", parts: [{ text: "Hi Bob!" }] },
];
const chat = ai.chats.create({
  model: "gemini-2.0-flash",
  history: history,
});

// Count tokens for the current chat history.
const countTokensResponse = await ai.models.countTokens({
  model: "gemini-2.0-flash",
  contents: chat.getHistory(),
});
console.log(countTokensResponse.totalTokens);

const chatResponse = await chat.sendMessage({
  message: "In one sentence, explain how a computer works to a young child.",
});
console.log(chatResponse.usageMetadata);

// Add an extra user message to the history.
const extraMessage = {
  role: "user",
  parts: [{ text: "What is the meaning of life?" }],
};
const combinedHistory = chat.getHistory();
combinedHistory.push(extraMessage);
const combinedCountTokensResponse = await ai.models.countTokens({
  model: "gemini-2.0-flash",
  contents: combinedHistory,
});
console.log(
  "Combined history token count:",
  combinedCountTokensResponse.totalTokens,
);
count_tokens.js
 
model := client.GenerativeModel("gemini-1.5-flash")
cs := model.StartChat()

cs.History = []*genai.Content{
	{
		Parts: []genai.Part{
			genai.Text("Hi my name is Bob"),
		},
		Role: "user",
	},
	{
		Parts: []genai.Part{
			genai.Text("Hi Bob!"),
		},
		Role: "model",
	},
}

prompt := "Explain how a computer works to a young child."
resp, err := cs.SendMessage(ctx, genai.Text(prompt))
if err != nil {
	log.Fatal(err)
}

// On the response for SendMessage, use `UsageMetadata` 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`).
fmt.Println("prompt_token_count:", resp.UsageMetadata.PromptTokenCount)
fmt.Println("candidates_token_count:", resp.UsageMetadata.CandidatesTokenCount)
fmt.Println("total_token_count:", resp.UsageMetadata.TotalTokenCount)
// ( prompt_token_count: 25, candidates_token_count: 21, total_token_count: 46 )
docs-snippets_test.go
 
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:countTokens?key=$GEMINI_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"}],
        },
      ],
      }'
count_tokens.sh
 
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)
count_tokens.kt
 
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)")
CountTokens.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.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}');
count_tokens.dart
 
// 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);
count_tokens.java
PythonNode.jsGoקונכייהKotlinSwiftDartJava
from google import genai
import PIL.Image

client = genai.Client()
prompt = "Tell me about this image"
your_image_file = PIL.Image.open(media / "organ.jpg")

# Count tokens for combined text and inline image.
print(
    client.models.count_tokens(
        model="gemini-2.0-flash", contents=[prompt, your_image_file]
    )
)
# ( e.g., total_tokens: 263 )

response = client.models.generate_content(
    model="gemini-2.0-flash", contents=[prompt, your_image_file]
)
print(response.usage_metadata)
# ( e.g., prompt_token_count: 264, candidates_token_count: 80, total_token_count: 345 )
count_tokens.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const prompt = "Tell me about this image";
const imageBuffer = fs.readFileSync(path.join(media, "organ.jpg"));

// Convert buffer to base64 string.
const imageBase64 = imageBuffer.toString("base64");

// Build contents using createUserContent and createPartFromBase64.
const contents = createUserContent([
  prompt,
  createPartFromBase64(imageBase64, "image/jpeg"),
]);

const countTokensResponse = await ai.models.countTokens({
  model: "gemini-2.0-flash",
  contents: contents,
});
console.log(countTokensResponse.totalTokens);

const generateResponse = await ai.models.generateContent({
  model: "gemini-2.0-flash",
  contents: contents,
});
console.log(generateResponse.usageMetadata);
count_tokens.js
 
model := client.GenerativeModel("gemini-1.5-flash")
prompt := "Tell me about this image"
imageFile, err := os.ReadFile(filepath.Join(testDataDir, "personWorkingOnComputer.jpg"))
if err != nil {
	log.Fatal(err)
}
// Call `CountTokens` 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.
tokResp, err := model.CountTokens(ctx, genai.Text(prompt), genai.ImageData("jpeg", imageFile))
if err != nil {
	log.Fatal(err)
}
fmt.Println("total_tokens:", tokResp.TotalTokens)
// ( total_tokens: 264 )

resp, err := model.GenerateContent(ctx, genai.Text(prompt), genai.ImageData("jpeg", imageFile))
if err != nil {
	log.Fatal(err)
}

fmt.Println("prompt_token_count:", resp.UsageMetadata.PromptTokenCount)
fmt.Println("candidates_token_count:", resp.UsageMetadata.CandidatesTokenCount)
fmt.Println("total_token_count:", resp.UsageMetadata.TotalTokenCount)
// ( prompt_token_count: 264, candidates_token_count: 100, total_token_count: 364 )
docs-snippets_test.go
 
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:countTokens?key=$GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
            {"text": "Tell me about this instrument"},
            {
              "inline_data": {
                "mime_type":"image/jpeg",
                "data": "'$(base64 $B64FLAGS $IMG_PATH)'"
              }
            }
        ]
        }]
       }' 2> /dev/null
count_tokens.sh
 
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)
count_tokens.kt
 
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)")
CountTokens.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.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}');
}
count_tokens.dart
 
// 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);
count_tokens.java
PythonNode.jsGoקונכייה
from google import genai
import time

client = genai.Client()
prompt = "Tell me about this video"
your_file = client.files.upload(file=media / "Big_Buck_Bunny.mp4")

# Wait for the video to be processed.
while your_file.state.name == "PROCESSING":
    print("processing video...")
    time.sleep(5)
    your_file = client.files.get(name=your_file.name)

print(
    client.models.count_tokens(
        model="gemini-2.0-flash", contents=[prompt, your_file]
    )
)
# ( e.g., total_tokens: 300 )

response = client.models.generate_content(
    model="gemini-2.0-flash", contents=[prompt, your_file]
)
print(response.usage_metadata)
# ( e.g., prompt_token_count: 301, candidates_token_count: 60, total_token_count: 361 )
count_tokens.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const prompt = "Tell me about this video";
let videoFile = await ai.files.upload({
  file: path.join(media, "Big_Buck_Bunny.mp4"),
  config: { mimeType: "video/mp4" },
});

// Poll until the video file is completely processed (state becomes ACTIVE).
while (!videoFile.state || videoFile.state.toString() !== "ACTIVE") {
  console.log("Processing video...");
  console.log("File state: ", videoFile.state);
  await sleep(5000);
  videoFile = await ai.files.get({ name: videoFile.name });
}

const countTokensResponse = await ai.models.countTokens({
  model: "gemini-2.0-flash",
  contents: createUserContent([
    prompt,
    createPartFromUri(videoFile.uri, videoFile.mimeType),
  ]),
});
console.log(countTokensResponse.totalTokens);

const generateResponse = await ai.models.generateContent({
  model: "gemini-2.0-flash",
  contents: createUserContent([
    prompt,
    createPartFromUri(videoFile.uri, videoFile.mimeType),
  ]),
});
console.log(generateResponse.usageMetadata);
count_tokens.js
 
model := client.GenerativeModel("gemini-1.5-flash")
prompt := "Tell me about this video"
file, err := client.UploadFileFromPath(ctx, filepath.Join(testDataDir, "earth.mp4"), nil)
if err != nil {
	log.Fatal(err)
}
defer client.DeleteFile(ctx, file.Name)

fd := genai.FileData{URI: file.URI}
// Call `CountTokens` to get the 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.
tokResp, err := model.CountTokens(ctx, genai.Text(prompt), fd)
if err != nil {
	log.Fatal(err)
}
fmt.Println("total_tokens:", tokResp.TotalTokens)
// ( total_tokens: 1481 )

resp, err := model.GenerateContent(ctx, genai.Text(prompt), fd)
if err != nil {
	log.Fatal(err)
}

fmt.Println("prompt_token_count:", resp.UsageMetadata.PromptTokenCount)
fmt.Println("candidates_token_count:", resp.UsageMetadata.CandidatesTokenCount)
fmt.Println("total_token_count:", resp.UsageMetadata.TotalTokenCount)
// ( prompt_token_count: 1481, candidates_token_count: 43, total_token_count: 1524 )

docs-snippets_test.go
 

MIME_TYPE=$(file -b --mime-type "${VIDEO_PATH}")
NUM_BYTES=$(wc -c < "${VIDEO_PATH}")
DISPLAY_NAME=VIDEO_PATH

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "${BASE_URL}/upload/v1beta/files?key=${GOOGLE_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${VIDEO_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq ".file.uri" file_info.json)

state=$(jq ".file.state" file_info.json)

name=$(jq ".file.name" file_info.json)

while [[ "($state)" = *"PROCESSING"* ]];
do
  echo "Processing video..."
  sleep 5
  # Get the file of interest to check state
  curl https://generativelanguage.googleapis.com/v1beta/files/$name > file_info.json
  state=$(jq ".file.state" file_info.json)
done

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:countTokens?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Describe this video clip"},
          {"file_data":{"mime_type": "video/mp4", "file_uri": '$file_uri'}}]
        }]
       }'
count_tokens.sh
Python
from google import genai

client = genai.Client()
sample_pdf = client.files.upload(file=media / "test.pdf")
token_count = client.models.count_tokens(
    model="gemini-2.0-flash",
    contents=["Give me a summary of this document.", sample_pdf],
)
print(f"{token_count=}")

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=["Give me a summary of this document.", sample_pdf],
)
print(response.usage_metadata)
count_tokens.py
PythonNode.jsGo
from google import genai
from google.genai import types
import time

client = genai.Client()
your_file = client.files.upload(file=media / "a11.txt")

cache = client.caches.create(
    model="gemini-1.5-flash-001",
    config={
        "contents": ["Here the Apollo 11 transcript:", your_file],
        "system_instruction": None,
        "tools": None,
    },
)

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

# Count tokens for the prompt (the cached content is not passed here).
print(client.models.count_tokens(model="gemini-2.0-flash", contents=prompt))
# ( e.g., total_tokens: 9 )

response = client.models.generate_content(
    model="gemini-1.5-flash-001",
    contents=prompt,
    config=types.GenerateContentConfig(
        cached_content=cache.name,
    ),
)
print(response.usage_metadata)
# ( e.g., prompt_token_count: ..., cached_content_token_count: ..., candidates_token_count: ... )
client.caches.delete(name=cache.name)
count_tokens.py
 
  // Make sure to include the following import:
  // import {GoogleGenAI} from '@google/genai';
  const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
  const textFile = await ai.files.upload({
    file: path.join(media, "a11.txt"),
    config: { mimeType: "text/plain" },
  });

  const cache = await ai.caches.create({
    model: "gemini-1.5-flash-001",
    config: {
      contents: createUserContent([
        "Here the Apollo 11 transcript:",
        createPartFromUri(textFile.uri, textFile.mimeType),
      ]),
      system_instruction: null,
      tools: null,
    },
  });

  const prompt = "Please give a short summary of this file.";
  const countTokensResponse = await ai.models.countTokens({
    model: "gemini-2.0-flash",
    contents: prompt,
  });
  console.log(countTokensResponse.totalTokens);

  const generateResponse = await ai.models.generateContent({
    model: "gemini-1.5-flash-001",
    contents: prompt,
    config: { cachedContent: cache.name },
  });
  console.log(generateResponse.usageMetadata);

  await ai.caches.delete({ name: cache.name });
  return {
    totalTokens: countTokensResponse.totalTokens,
    usage: generateResponse.usageMetadata,
  };
}

// Error: systemInstruction parameter is not supported in Gemini API.
export async function tokensSystemInstruction() {
  // Make sure to include the following import:
  // import {GoogleGenAI} from '@google/genai';
  const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
  const prompt = "The quick brown fox jumps over the lazy dog.";
  const countTokensResponse = await ai.models.countTokens({
    model: "gemini-2.0-flash",
    contents: prompt,
  });
  console.log(
    "base_count (no system instruction):",
    countTokensResponse.totalTokens,
  );

  // Uncomment if the API gets support for systemInstruction in count_tokens.
  /*
  const countTokensWithSysInstResponse = await ai.models.countTokens({
    model: "gemini-2.0-flash",
    contents: prompt,
    config: {systemInstruction: 'You are a cat. Your name is Neko.'},
  });
  console.log(
    "total_tokens (with system instruction):",
    countTokensWithSysInstResponse.totalTokens,
  );
  */
  return {
    responseTokenCount: countTokensResponse.totalTokens,
    // Uncomment if the API gets support for systemInstruction in count_tokens.
    // sysInstResponseTokenCount: countTokensWithSysInstResponse.totalTokens,
  };
}

// Error: tools parameter is not supported in Gemini API.
export async function tokensTools() {
  // Make sure to include the following import:
  // import {GoogleGenAI} from '@google/genai';
  const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
  const prompt =
    "I have 57 cats, each owns 44 mittens, how many mittens is that in total?";
  const countTokensResponse = await ai.models.countTokens({
    model: "gemini-2.0-flash",
    contents: prompt,
  });
  console.log("total_tokens (no tools):", countTokensResponse.totalTokens);

  // Define function declarations for arithmetic operations.
  const addDeclaration = {
    name: "addNumbers",
    parameters: {
      type: "object",
      description: "Return the result of adding two numbers.",
      properties: {
        firstParam: {
          type: "number",
          description:
            "The first parameter which can be an integer or a floating point number.",
        },
        secondParam: {
          type: "number",
          description:
            "The second parameter which can be an integer or a floating point number.",
        },
      },
      required: ["firstParam", "secondParam"],
    },
  };

  const subtractDeclaration = {
    name: "subtractNumbers",
    parameters: {
      type: "object",
      description:
        "Return the result of subtracting the second number from the first.",
      properties: {
        firstParam: {
          type: "number",
          description: "The first parameter.",
        },
        secondParam: {
          type: "number",
          description: "The second parameter.",
        },
      },
      required: ["firstParam", "secondParam"],
    },
  };

  const multiplyDeclaration = {
    name: "multiplyNumbers",
    parameters: {
      type: "object",
      description: "Return the product of two numbers.",
      properties: {
        firstParam: {
          type: "number",
          description: "The first parameter.",
        },
        secondParam: {
          type: "number",
          description: "The second parameter.",
        },
      },
      required: ["firstParam", "secondParam"],
    },
  };

  const divideDeclaration = {
    name: "divideNumbers",
    parameters: {
      type: "object",
      description:
        "Return the quotient of dividing the first number by the second.",
      properties: {
        firstParam: {
          type: "number",
          description: "The first parameter.",
        },
        secondParam: {
          type: "number",
          description: "The second parameter.",
        },
      },
      required: ["firstParam", "secondParam"],
    },
  };

  const tools = {
    functionDeclarations: [
      addDeclaration,
      subtractDeclaration,
      multiplyDeclaration,
      divideDeclaration,
    ],
  };

  // Uncomment if the API gets support for tools in count_tokens.
  /*
  const countTokensWithToolsResponse = await ai.models.countTokens({
    model: "gemini-2.0-flash",
    contents: prompt,
    config: { tools: tools }
  });
  console.log("total_tokens (with tools):", countTokensWithToolsResponse.totalTokens);
  */
  return {
    responseTokenCount: countTokensResponse.totalTokens,
    // Uncomment if the API gets support for tools in count_tokens.
    // toolsResponseTokenCount : countTokensWithToolsResponse.totalTokens
  };
}

count_tokens.js
 
txt := strings.Repeat("George Washington was the first president of the United States. ", 3000)
argcc := &genai.CachedContent{
	Model:    "gemini-1.5-flash-001",
	Contents: []*genai.Content{genai.NewUserContent(genai.Text(txt))},
}
cc, err := client.CreateCachedContent(ctx, argcc)
if err != nil {
	log.Fatal(err)
}
defer client.DeleteCachedContent(ctx, cc.Name)

modelWithCache := client.GenerativeModelFromCachedContent(cc)
prompt := "Summarize this statement"
tokResp, err := modelWithCache.CountTokens(ctx, genai.Text(prompt))
if err != nil {
	log.Fatal(err)
}
fmt.Println("total_tokens:", tokResp.TotalTokens)
// ( total_tokens: 5 )

resp, err := modelWithCache.GenerateContent(ctx, genai.Text(prompt))
if err != nil {
	log.Fatal(err)
}

fmt.Println("prompt_token_count:", resp.UsageMetadata.PromptTokenCount)
fmt.Println("candidates_token_count:", resp.UsageMetadata.CandidatesTokenCount)
fmt.Println("cached_content_token_count:", resp.UsageMetadata.CachedContentTokenCount)
fmt.Println("total_token_count:", resp.UsageMetadata.TotalTokenCount)
// ( prompt_token_count: 33007,  candidates_token_count: 39, cached_content_token_count: 33002, total_token_count: 33046 )
docs-snippets_test.go
PythonNode.jsGoKotlinSwiftDartJava
from google import genai
from google.genai import types

client = genai.Client()
prompt = "The quick brown fox jumps over the lazy dog."

base_count = client.models.count_tokens(
    model="gemini-2.0-flash", contents=prompt
)
print("total_tokens (no system instruction):", base_count)
# ( e.g., total_tokens: 10 )

# When using a system instruction, include it in the count tokens config.
# TODO: Uncomment once the API stops failing

# count_with_sys = client.models.count_tokens(
#     model="gemini-2.0-flash",
#     contents=prompt,
#     config=types.CountTokensConfig(system_instruction="You are a cat. Your name is Neko.")
# )
# print("total_tokens (with system instruction):", count_with_sys)
# ( e.g., total_tokens: 21 )
count_tokens.py
 
  // Make sure to include the following import:
  // import {GoogleGenAI} from '@google/genai';
  const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
  const prompt = "The quick brown fox jumps over the lazy dog.";
  const countTokensResponse = await ai.models.countTokens({
    model: "gemini-2.0-flash",
    contents: prompt,
  });
  console.log(
    "base_count (no system instruction):",
    countTokensResponse.totalTokens,
  );

  // Uncomment if the API gets support for systemInstruction in count_tokens.
  /*
  const countTokensWithSysInstResponse = await ai.models.countTokens({
    model: "gemini-2.0-flash",
    contents: prompt,
    config: {systemInstruction: 'You are a cat. Your name is Neko.'},
  });
  console.log(
    "total_tokens (with system instruction):",
    countTokensWithSysInstResponse.totalTokens,
  );
  */
  return {
    responseTokenCount: countTokensResponse.totalTokens,
    // Uncomment if the API gets support for systemInstruction in count_tokens.
    // sysInstResponseTokenCount: countTokensWithSysInstResponse.totalTokens,
  };
}

// Error: tools parameter is not supported in Gemini API.
export async function tokensTools() {
  // Make sure to include the following import:
  // import {GoogleGenAI} from '@google/genai';
  const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
  const prompt =
    "I have 57 cats, each owns 44 mittens, how many mittens is that in total?";
  const countTokensResponse = await ai.models.countTokens({
    model: "gemini-2.0-flash",
    contents: prompt,
  });
  console.log("total_tokens (no tools):", countTokensResponse.totalTokens);

  // Define function declarations for arithmetic operations.
  const addDeclaration = {
    name: "addNumbers",
    parameters: {
      type: "object",
      description: "Return the result of adding two numbers.",
      properties: {
        firstParam: {
          type: "number",
          description:
            "The first parameter which can be an integer or a floating point number.",
        },
        secondParam: {
          type: "number",
          description:
            "The second parameter which can be an integer or a floating point number.",
        },
      },
      required: ["firstParam", "secondParam"],
    },
  };

  const subtractDeclaration = {
    name: "subtractNumbers",
    parameters: {
      type: "object",
      description:
        "Return the result of subtracting the second number from the first.",
      properties: {
        firstParam: {
          type: "number",
          description: "The first parameter.",
        },
        secondParam: {
          type: "number",
          description: "The second parameter.",
        },
      },
      required: ["firstParam", "secondParam"],
    },
  };

  const multiplyDeclaration = {
    name: "multiplyNumbers",
    parameters: {
      type: "object",
      description: "Return the product of two numbers.",
      properties: {
        firstParam: {
          type: "number",
          description: "The first parameter.",
        },
        secondParam: {
          type: "number",
          description: "The second parameter.",
        },
      },
      required: ["firstParam", "secondParam"],
    },
  };

  const divideDeclaration = {
    name: "divideNumbers",
    parameters: {
      type: "object",
      description:
        "Return the quotient of dividing the first number by the second.",
      properties: {
        firstParam: {
          type: "number",
          description: "The first parameter.",
        },
        secondParam: {
          type: "number",
          description: "The second parameter.",
        },
      },
      required: ["firstParam", "secondParam"],
    },
  };

  const tools = {
    functionDeclarations: [
      addDeclaration,
      subtractDeclaration,
      multiplyDeclaration,
      divideDeclaration,
    ],
  };

  // Uncomment if the API gets support for tools in count_tokens.
  /*
  const countTokensWithToolsResponse = await ai.models.countTokens({
    model: "gemini-2.0-flash",
    contents: prompt,
    config: { tools: tools }
  });
  console.log("total_tokens (with tools):", countTokensWithToolsResponse.totalTokens);
  */
  return {
    responseTokenCount: countTokensResponse.totalTokens,
    // Uncomment if the API gets support for tools in count_tokens.
    // toolsResponseTokenCount : countTokensWithToolsResponse.totalTokens
  };
}

count_tokens.js
 
model := client.GenerativeModel("gemini-1.5-flash")
prompt := "The quick brown fox jumps over the lazy dog"

respNoInstruction, err := model.CountTokens(ctx, genai.Text(prompt))
if err != nil {
	log.Fatal(err)
}
fmt.Println("total_tokens:", respNoInstruction.TotalTokens)
// ( total_tokens: 10 )

// The total token count includes everything sent to the GenerateContent
// request. When you use system instructions, the total token
// count increases.
model.SystemInstruction = genai.NewUserContent(genai.Text("You are a cat. Your name is Neko."))
respWithInstruction, err := model.CountTokens(ctx, genai.Text(prompt))
if err != nil {
	log.Fatal(err)
}
fmt.Println("total_tokens:", respWithInstruction.TotalTokens)
// ( total_tokens: 21 )
docs-snippets_test.go
 
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)
count_tokens.kt
 
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)")
CountTokens.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.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}');
count_tokens.dart
 
// 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);
count_tokens.java
PythonNode.jsKotlinSwiftDartJava
from google import genai
from google.genai import types

client = genai.Client()
prompt = (
    "I have 57 cats, each owns 44 mittens, how many mittens is that in total?"
)
print(client.models.count_tokens(model="gemini-2.0-flash", contents=prompt))
# ( e.g., total_tokens: 22 )

# Define the function declarations for the arithmetic operations
add_function = types.FunctionDeclaration(
    name="add",
    description="Return the sum of a and b",
    parameters=types.Schema(
        type="OBJECT",
        properties={
            "a": types.Schema(
                type="NUMBER",
                description="The first number",
            ),
            "b": types.Schema(
                type="NUMBER",
                description="The second number",
            ),
        },
        required=["a", "b"],
    ),
)

subtract_function = types.FunctionDeclaration(
    name="subtract",
    description="Return the difference of a and b (a - b)",
    parameters=types.Schema(
        type="OBJECT",
        properties={
            "a": types.Schema(
                type="NUMBER",
                description="The first number",
            ),
            "b": types.Schema(
                type="NUMBER",
                description="The second number",
            ),
        },
        required=["a", "b"],
    ),
)

multiply_function = types.FunctionDeclaration(
    name="multiply",
    description="Return the product of a and b",
    parameters=types.Schema(
        type="OBJECT",
        properties={
            "a": types.Schema(
                type="NUMBER",
                description="The first number",
            ),
            "b": types.Schema(
                type="NUMBER",
                description="The second number",
            ),
        },
        required=["a", "b"],
    ),
)

divide_function = types.FunctionDeclaration(
    name="divide",
    description="Return the quotient of a divided by b",
    parameters=types.Schema(
        type="OBJECT",
        properties={
            "a": types.Schema(
                type="NUMBER",
                description="The numerator",
            ),
            "b": types.Schema(
                type="NUMBER",
                description="The denominator (must not be zero)",
            ),
        },
        required=["a", "b"],
    ),
)
tools = [
    types.Tool(function_declarations=[add_function]),
    types.Tool(function_declarations=[subtract_function]),
    types.Tool(function_declarations=[multiply_function]),
    types.Tool(function_declarations=[divide_function]),
]

# Count tokens when tools are included. Tools increase the token count.
# TODO: Uncomment once the API stops failing
# tokens_with_tools = client.models.count_tokens(
#     model="gemini-2.0-flash",
#     contents=prompt,
#     config=types.CountTokensConfig(tools=tools)
# )
# print(tokens_with_tools)
# ( e.g., total_tokens: 206 )
count_tokens.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const prompt =
  "I have 57 cats, each owns 44 mittens, how many mittens is that in total?";
const countTokensResponse = await ai.models.countTokens({
  model: "gemini-2.0-flash",
  contents: prompt,
});
console.log("total_tokens (no tools):", countTokensResponse.totalTokens);

// Define function declarations for arithmetic operations.
const addDeclaration = {
  name: "addNumbers",
  parameters: {
    type: "object",
    description: "Return the result of adding two numbers.",
    properties: {
      firstParam: {
        type: "number",
        description:
          "The first parameter which can be an integer or a floating point number.",
      },
      secondParam: {
        type: "number",
        description:
          "The second parameter which can be an integer or a floating point number.",
      },
    },
    required: ["firstParam", "secondParam"],
  },
};

const subtractDeclaration = {
  name: "subtractNumbers",
  parameters: {
    type: "object",
    description:
      "Return the result of subtracting the second number from the first.",
    properties: {
      firstParam: {
        type: "number",
        description: "The first parameter.",
      },
      secondParam: {
        type: "number",
        description: "The second parameter.",
      },
    },
    required: ["firstParam", "secondParam"],
  },
};

const multiplyDeclaration = {
  name: "multiplyNumbers",
  parameters: {
    type: "object",
    description: "Return the product of two numbers.",
    properties: {
      firstParam: {
        type: "number",
        description: "The first parameter.",
      },
      secondParam: {
        type: "number",
        description: "The second parameter.",
      },
    },
    required: ["firstParam", "secondParam"],
  },
};

const divideDeclaration = {
  name: "divideNumbers",
  parameters: {
    type: "object",
    description:
      "Return the quotient of dividing the first number by the second.",
    properties: {
      firstParam: {
        type: "number",
        description: "The first parameter.",
      },
      secondParam: {
        type: "number",
        description: "The second parameter.",
      },
    },
    required: ["firstParam", "secondParam"],
  },
};

const tools = {
  functionDeclarations: [
    addDeclaration,
    subtractDeclaration,
    multiplyDeclaration,
    divideDeclaration,
  ],
};

// Uncomment if the API gets support for tools in count_tokens.
/*
const countTokensWithToolsResponse = await ai.models.countTokens({
  model: "gemini-2.0-flash",
  contents: prompt,
  config: { tools: tools }
});
console.log("total_tokens (with tools):", countTokensWithToolsResponse.totalTokens);
*/
count_tokens.js
 
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)
count_tokens.kt
 
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)")
CountTokens.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.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}');
count_tokens.dart
 
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);
count_tokens.java

גוף התשובה

תשובה מ-models.countTokens.

הפונקציה מחזירה את הערך של tokenCount של המודל עבור prompt.

אם הפעולה מצליחה, גוף התגובה מכיל נתונים במבנה הבא:

שדות
totalTokens integer

מספר הטוקנים שה-Model ממיר את ה-prompt אליהם. תמיד מספר חיובי.

cachedContentTokenCount integer

מספר האסימונים בחלק של ההנחיה שנשמר במטמון (התוכן שנשמר במטמון).

promptTokensDetails[] object (ModalityTokenCount)

פלט בלבד. רשימת המודלים שעברו עיבוד בקלט הבקשה.

cacheTokensDetails[] object (ModalityTokenCount)

פלט בלבד. רשימת המודלים שעברו עיבוד בתוכן שנשמר במטמון.

ייצוג ב-JSON 
{
  "totalTokens": integer,
  "cachedContentTokenCount": integer,
  "promptTokensDetails": [
    {
      object (ModalityTokenCount)
    }
  ],
  "cacheTokensDetails": [
    {
      object (ModalityTokenCount)
    }
  ]
}

GenerateContentRequest

שולחים בקשה ליצירת השלמה מהמודל.

שדות
model string

חובה. השם של Model שמשמש ליצירת ההשלמה.

פורמט: models/{model}

contents[] object (Content)

חובה. תוכן השיחה הנוכחית עם המודל.

בשאילתות עם תור אחד, זהו מופע יחיד. בשאילתות עם מספר תשובות, כמו chat, זהו שדה חוזר שמכיל את היסטוריית השיחה ואת הבקשה האחרונה.

tools[] object (Tool)

זה שינוי אופציונלי. רשימה של Tools ש-Model עשוי להשתמש בה כדי ליצור את התשובה הבאה.

Tool הוא קטע קוד שמאפשר למערכת לקיים אינטראקציה עם מערכות חיצוניות כדי לבצע פעולה או קבוצת פעולות מחוץ לידע ולהיקף של Model. הערכים הנתמכים של Tool הם Function ו-codeExecution. מידע נוסף זמין במדריך בנושא קריאה לפונקציה ובמדריך בנושא ביצוע קוד.

toolConfig object (ToolConfig)

זה שינוי אופציונלי. הגדרת הכלי לכל Tool שצוין בבקשה. במדריך לקריאה לפונקציות מופיעה דוגמה לשימוש.

safetySettings[] object (SafetySetting)

זה שינוי אופציונלי. רשימה של מופעים ייחודיים של SafetySetting לחסימת תוכן לא בטוח.

המדיניות הזו תיאכף ב-GenerateContentRequest.contents וב-GenerateContentResponse.candidates. אסור שיהיה יותר מהגדרה אחת לכל סוג SafetyCategory. ה-API יחסום את כל התכנים והתשובות שלא עומדים בערכי הסף שהוגדרו בהגדרות האלה. הרשימה הזו מבטלת את הגדרות ברירת המחדל של כל SafetyCategory שצוין ב-safetySettings. אם לא צוין SafetySetting ל-SafetyCategory מסוים ברשימה, ממשק ה-API ישתמש בהגדרת הבטיחות שמוגדרת כברירת מחדל לקטגוריה הזו. המערכת תומכת בקטגוריות הנזק HARM_CATEGORY_HATE_SPEECH,‏ HARM_CATEGORY_SEXUALLY_EXPLICIT,‏ HARM_CATEGORY_DANGEROUS_CONTENT,‏ HARM_CATEGORY_HARASSMENT ו-HARM_CATEGORY_CIVIC_INTEGRITY. במדריך מפורט מידע על הגדרות הבטיחות הזמינות. מומלץ גם לעיין בהנחיות הבטיחות כדי ללמוד איך לשלב שיקולי בטיחות באפליקציות ה-AI.

systemInstruction object (Content)

זה שינוי אופציונלי. המפתח מגדיר הוראות מערכת. בשלב הזה, רק טקסט.

generationConfig object (GenerationConfig)

זה שינוי אופציונלי. אפשרויות הגדרה ליצירת מודלים ולפלטים.

cachedContent string

זה שינוי אופציונלי. השם של התוכן ששמור במטמון כדי לשמש כהקשר להצגת התחזית. פורמט: cachedContents/{cachedContent}

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