Etkileşimler API'si artık genel kullanıma sunulmuştur. En yeni özelliklere ve modellere erişmek için bu API'yi kullanmanızı öneririz.

Google, içerikleri tercih ettiğiniz dile çevirmek için yapay zeka teknolojisini kullanır. Yapay zeka çevirilerinde hata olabilir.

Google GenAI SDK'ya geçiş

2024'ün sonlarında Gemini 2.0'ın yayınlanmasıyla birlikte Google GenAI SDK adlı yeni bir kitaplık grubu kullanıma sunuldu. Güncellenmiş istemci mimarisi sayesinde geliştirici deneyimini iyileştirir ve geliştirici ile kurumsal iş akışları arasındaki geçişi kolaylaştırır.

Google GenAI SDK artık desteklenen tüm platformlarda genel kullanıma sunulmuştur. Eski kitaplıklarımızdan birini kullanıyorsanız geçiş yapmanızı önemle tavsiye ederiz.

Bu kılavuz, başlamanıza yardımcı olmak için taşınan kodun öncesi ve sonrası örneklerini sunar.

Kurulum

Önce

Python

pip install -U -q "google-generativeai"

JavaScript

npm install @google/generative-ai

Go

go get github.com/google/generative-ai-go

Java

<dependency>
    <groupId>com.google.ai.client.generativeai</groupId>
    <artifactId>generativeai</artifactId>
    <version>0.9.0</version>
</dependency>

Sonra

Python

pip install -U -q "google-genai"

JavaScript

npm install @google/genai

Go

go get google.golang.org/genai

Java

<dependency>
    <groupId>com.google.genai</groupId>
    <artifactId>google-genai</artifactId>
    <version>1.67.0</version>
</dependency>

API erişimi

Eski SDK, çeşitli geçici yöntemler kullanarak API istemcisini arka planda örtülü olarak işliyordu. Bu durum, müşteriyi ve kimlik bilgilerini yönetmeyi zorlaştırıyordu. Artık merkezi bir Client nesnesi üzerinden etkileşim kurarsınız. Bu Client nesnesi, çeşitli API hizmetleri (ör.models, chats, files, tunings) için tek bir giriş noktası görevi görür. Tutarlılığı artırır ve farklı API çağrıları arasında kimlik bilgisi ve yapılandırma yönetimini basitleştirir.

Öncesi (Daha Az Merkezi API Erişimi)

Python

Eski SDK, çoğu API çağrısı için açıkça üst düzey bir istemci nesnesi kullanmıyordu. Doğrudan GenerativeModel nesnelerini oluşturup bunlarla etkileşim kurarsınız.

import google.generativeai as genai

# Directly create and use model objects
model = genai.GenerativeModel('gemini-3.8-flash')
response = model.generate_content(...)
chat = model.start_chat(...)

JavaScript

GoogleGenerativeAI, modeller ve sohbet için merkezi bir nokta olsa da dosya ve önbellek yönetimi gibi diğer işlevler genellikle tamamen ayrı istemci sınıflarının içe aktarılmasını ve oluşturulmasını gerektiriyordu.

import { GoogleGenerativeAI } from "@google/generative-ai";
import { GoogleAIFileManager, GoogleAICacheManager } from "@google/generative-ai/server"; // For files/caching

const genAI = new GoogleGenerativeAI("GEMINI_API_KEY");
const fileManager = new GoogleAIFileManager("GEMINI_API_KEY");
const cacheManager = new GoogleAICacheManager("GEMINI_API_KEY");

// Get a model instance, then call methods on it
const model = genAI.getGenerativeModel({ model: "gemini-3.8-flash" });
const result = await model.generateContent(...);
const chat = model.startChat(...);

// Call methods on separate client objects for other services
const uploadedFile = await fileManager.uploadFile(...);
const cache = await cacheManager.create(...);

Java

import com.google.genai.Chat;
import com.google.genai.Client;
import com.google.genai.types.GenerateContentResponse;

// Previously, model operations were called on separate model instances
Client client = new Client();
GenerateContentResponse response =
    client.models.generateContent("gemini-3.8-flash", "Tell me a story.", null);
Chat chat = client.chats.create("gemini-3.8-flash");

Go

genai.NewClient işlevi bir istemci oluşturdu ancak üretken model işlemleri genellikle bu istemciden alınan ayrı bir GenerativeModel örneğinde çağrıldı. Diğer hizmetlere farklı paketler veya kalıplar üzerinden erişilmiş olabilir.

import (
      "github.com/google/generative-ai-go/genai"
      "github.com/google/generative-ai-go/genai/fileman" // For files
      "google.golang.org/api/option"
)

client, err := genai.NewClient(ctx, option.WithAPIKey("GEMINI_API_KEY"))
fileClient, err := fileman.NewClient(ctx, option.WithAPIKey("GEMINI_API_KEY"))

// Get a model instance, then call methods on it
model := client.GenerativeModel("gemini-3.8-flash")
resp, err := model.GenerateContent(...)
cs := model.StartChat()

// Call methods on separate client objects for other services
uploadedFile, err := fileClient.UploadFile(...)

Sonra (Merkezi Müşteri Nesnesi)

Python

from google import genai

# Create a single client object
client = genai.Client()

# Access API methods through services on the client object
response = client.models.generate_content(...)
chat = client.chats.create(...)
my_file = client.files.upload(...)
tuning_job = client.tunings.tune(...)

JavaScript

import { GoogleGenAI } from "@google/genai";

// Create a single client object
const ai = new GoogleGenAI({apiKey: "GEMINI_API_KEY"});

// Access API methods through services on the client object
const response = await ai.models.generateContent(...);
const chat = ai.chats.create(...);
const uploadedFile = await ai.files.upload(...);
const cache = await ai.caches.create(...);

Java

import com.google.genai.Chat;
import com.google.genai.Client;
import com.google.genai.types.CachedContent;
import com.google.genai.types.CreateCachedContentConfig;
import com.google.genai.types.File;
import com.google.genai.types.GenerateContentResponse;

// Create a single client object
Client client = new Client();

// Access API methods through services on the client object
GenerateContentResponse response =
    client.models.generateContent("gemini-3.8-flash", "Tell me a story.", null);
Chat chat = client.chats.create("gemini-3.8-flash");
File uploadedFile = client.files.upload("sample.txt", null);
CachedContent cache =
    client.caches.create("gemini-3.8-flash", CreateCachedContentConfig.builder().build());

Go

import "google.golang.org/genai"

// Create a single client object
client, err := genai.NewClient(ctx, nil)

// Access API methods through services on the client object
result, err := client.Models.GenerateContent(...)
chat, err := client.Chats.Create(...)
uploadedFile, err := client.Files.Upload(...)
tuningJob, err := client.Tunings.Tune(...)

Kimlik doğrulama

Hem eski hem de yeni kitaplıklar, API anahtarları kullanılarak kimlik doğrular. API anahtarınızı Google AI Studio'da oluşturabilirsiniz.

Önce

Python

Eski SDK, API istemci nesnesini örtülü olarak işliyordu.

import google.generativeai as genai

genai.configure(api_key=...)

JavaScript

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI("GEMINI_API_KEY");

Java

import com.google.genai.Client;

// Passing the API key explicitly to the client builder
Client client = Client.builder().apiKey("GEMINI_API_KEY").build();

Go

Google kitaplıklarını içe aktarın:

import (
      "github.com/google/generative-ai-go/genai"
      "google.golang.org/api/option"
)

İstemciyi oluşturun:

client, err := genai.NewClient(ctx, option.WithAPIKey("GEMINI_API_KEY"))

Sonra

Python

Google GenAI SDK ile önce API'yi çağırmak için kullanılan bir API istemcisi oluşturursunuz. Yeni SDK, istemciye iletmediğiniz takdirde API anahtarınızı GEMINI_API_KEY ortam değişkenlerinden alır.

export GEMINI_API_KEY="YOUR_API_KEY"

from google import genai

client = genai.Client() # Set the API key using the GEMINI_API_KEY env var.
                        # Alternatively, you could set the API key explicitly:
                        # client = genai.Client(api_key="YOUR_API_KEY")

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({apiKey: "GEMINI_API_KEY"});

Java

import com.google.genai.Client;

// The client automatically picks up the GEMINI_API_KEY environment variable,
// or you can pass it explicitly via Client.builder().apiKey("GEMINI_API_KEY").build()
Client client = new Client();

Go

GenAI kitaplığını içe aktarın:

import "google.golang.org/genai"

İstemciyi oluşturun:

client, err := genai.NewClient(ctx, &genai.ClientConfig{
        Backend:  genai.BackendGeminiAPI,
})

İçerik oluşturma

Metin

Önce

Python

Daha önce istemci nesneleri yoktu ve API'lere doğrudan GenerativeModel nesneleri üzerinden erişiyordunuz.

import google.generativeai as genai

model = genai.GenerativeModel('gemini-3.8-flash')
response = model.generate_content(
    'Tell me a story in 300 words'
)
print(response.text)

JavaScript

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-3.8-flash" });
const prompt = "Tell me a story in 300 words";

const result = await model.generateContent(prompt);
console.log(result.response.text());

Java

import com.google.genai.Client;
import com.google.genai.types.GenerateContentResponse;

Client client = new Client();
String prompt = "Tell me a story in 300 words";

GenerateContentResponse response =
    client.models.generateContent("gemini-3.8-flash", prompt, null);
System.out.println(response.text());

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, option.WithAPIKey("GEMINI_API_KEY"))
if err != nil {
    log.Fatal(err)
}
defer client.Close()

model := client.GenerativeModel("gemini-3.8-flash")
resp, err := model.GenerateContent(ctx, genai.Text("Tell me a story in 300 words."))
if err != nil {
    log.Fatal(err)
}

printResponse(resp) // utility for printing response parts

Sonra

Python

Yeni Google GenAI SDK, Client nesnesi aracılığıyla tüm API yöntemlerine erişim sağlar. Birkaç durum bilgisi içeren özel durum (chat ve live-api session) hariç, bunların tümü durum bilgisi içermeyen işlevlerdir. Nesneler, kolaylık ve tutarlılık için pydantic sınıfları olarak döndürülür.

from google import genai
client = genai.Client()

response = client.models.generate_content(
    model='gemini-3.8-flash',
    contents='Tell me a story in 300 words.'
)
print(response.text)

print(response.model_dump_json(
    exclude_none=True, indent=4))

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

const response = await ai.models.generateContent({
  model: "gemini-3.8-flash",
  contents: "Tell me a story in 300 words.",
});
console.log(response.text);

Java

import com.google.genai.Client;
import com.google.genai.types.GenerateContentResponse;

Client client = new Client();

GenerateContentResponse response =
    client.models.generateContent(
        "gemini-3.8-flash", "Tell me a story in 300 words.", null);
System.out.println(response.text());

Go

ctx := context.Background()
  client, err := genai.NewClient(ctx, nil)
if err != nil {
    log.Fatal(err)
}

result, err := client.Models.GenerateContent(ctx, "gemini-3.8-flash", genai.Text("Tell me a story in 300 words."), nil)
if err != nil {
    log.Fatal(err)
}
debugPrint(result) // utility for printing result

Resim

Önce

Python

import google.generativeai as genai

model = genai.GenerativeModel('gemini-3.8-flash')
response = model.generate_content([
    'Tell me a story based on this image',
    Image.open(image_path)
])
print(response.text)

JavaScript

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI("GEMINI_API_KEY");
const model = genAI.getGenerativeModel({ model: "gemini-3.8-flash" });

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

const prompt = "Tell me a story based on this image";

const imagePart = fileToGenerativePart(
  `path/to/organ.jpg`,
  "image/jpeg",
);

const result = await model.generateContent([prompt, imagePart]);
console.log(result.response.text());

Java

import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.nio.file.Files;
import java.nio.file.Paths;

Client client = new Client();

byte[] imageBytes = Files.readAllBytes(Paths.get("path/to/organ.jpg"));
Part imagePart = Part.fromBytes(imageBytes, "image/jpeg");

GenerateContentResponse response =
    client.models.generateContent(
        "gemini-3.8-flash",
        Content.fromParts(Part.fromText("Tell me a story based on this image"), imagePart),
        null);
System.out.println(response.text());

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, option.WithAPIKey("GEMINI_API_KEY"))
if err != nil {
    log.Fatal(err)
}
defer client.Close()

model := client.GenerativeModel("gemini-3.8-flash")

imgData, err := os.ReadFile("path/to/organ.jpg")
if err != nil {
    log.Fatal(err)
}

resp, err := model.GenerateContent(ctx,
    genai.Text("Tell me about this instrument"),
    genai.ImageData("jpeg", imgData))
if err != nil {
    log.Fatal(err)
}

printResponse(resp) // utility for printing response

Sonra

Python

Yeni SDK'da aynı kolaylık özelliklerinin çoğu bulunur. Örneğin, PIL.Image nesneleri otomatik olarak dönüştürülür.

from google import genai
from PIL import Image

client = genai.Client()

response = client.models.generate_content(
    model='gemini-3.8-flash',
    contents=[
        'Tell me a story based on this image',
        Image.open(image_path)
    ]
)
print(response.text)

JavaScript

import {GoogleGenAI} from '@google/genai';

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

const organ = await ai.files.upload({
  file: "path/to/organ.jpg",
});

const response = await ai.models.generateContent({
  model: "gemini-3.8-flash",
  contents: [
    createUserContent([
      "Tell me a story based on this image",
      createPartFromUri(organ.uri, organ.mimeType)
    ]),
  ],
});
console.log(response.text);

Java

import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.File;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;

Client client = new Client();

File organ = client.files.upload("path/to/organ.jpg", null);

GenerateContentResponse response =
    client.models.generateContent(
        "gemini-3.8-flash",
        Content.fromParts(
            Part.fromText("Tell me a story based on this image"),
            Part.fromUri(organ.uri().orElse(""), organ.mimeType().orElse("image/jpeg"))),
        null);
System.out.println(response.text());

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
    log.Fatal(err)
}

imgData, err := os.ReadFile("path/to/organ.jpg")
if err != nil {
    log.Fatal(err)
}

parts := []*genai.Part{
    {Text: "Tell me a story based on this image"},
    {InlineData: &genai.Blob{Data: imgData, MIMEType: "image/jpeg"}},
}
contents := []*genai.Content{
    {Parts: parts},
}

result, err := client.Models.GenerateContent(ctx, "gemini-3.8-flash", contents, nil)
if err != nil {
    log.Fatal(err)
}
debugPrint(result) // utility for printing result

Canlı Yayın

Önce

Python

import google.generativeai as genai

response = model.generate_content(
    "Write a cute story about cats.",
    stream=True)
for chunk in response:
    print(chunk.text)

JavaScript

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI("GEMINI_API_KEY");
const model = genAI.getGenerativeModel({ model: "gemini-3.8-flash" });

const prompt = "Write a story about a magic backpack.";

const result = await model.generateContentStream(prompt);

// Print text as it comes in.
for await (const chunk of result.stream) {
  const chunkText = chunk.text();
  process.stdout.write(chunkText);
}

Java

import com.google.genai.Client;
import com.google.genai.ResponseStream;
import com.google.genai.types.GenerateContentResponse;

Client client = new Client();
String prompt = "Write a story about a magic backpack.";

try (ResponseStream<GenerateContentResponse> stream =
    client.models.generateContentStream("gemini-3.8-flash", prompt, null)) {
  for (GenerateContentResponse chunk : stream) {
    System.out.print(chunk.text());
  }
}

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, option.WithAPIKey("GEMINI_API_KEY"))
if err != nil {
    log.Fatal(err)
}
defer client.Close()

model := client.GenerativeModel("gemini-3.8-flash")
iter := model.GenerateContentStream(ctx, genai.Text("Write a story about a magic backpack."))
for {
    resp, err := iter.Next()
    if err == iterator.Done {
        break
    }
    if err != nil {
        log.Fatal(err)
    }
    printResponse(resp) // utility for printing the response
}

Sonra

Python

from google import genai

client = genai.Client()

for chunk in client.models.generate_content_stream(
  model='gemini-3.8-flash',
  contents='Tell me a story in 300 words.'
):
    print(chunk.text)

JavaScript

import {GoogleGenAI} from '@google/genai';

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

const response = await ai.models.generateContentStream({
  model: "gemini-3.8-flash",
  contents: "Write a story about a magic backpack.",
});
let text = "";
for await (const chunk of response) {
  console.log(chunk.text);
  text += chunk.text;
}

Java

import com.google.genai.Client;
import com.google.genai.ResponseStream;
import com.google.genai.types.GenerateContentResponse;

Client client = new Client();

try (ResponseStream<GenerateContentResponse> response =
    client.models.generateContentStream(
        "gemini-3.8-flash", "Tell me a story in 300 words.", null)) {
  for (GenerateContentResponse chunk : response) {
    System.out.println(chunk.text());
  }
}

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
    log.Fatal(err)
}

for result, err := range client.Models.GenerateContentStream(
    ctx,
    "gemini-3.8-flash",
    genai.Text("Write a story about a magic backpack."),
    nil,
) {
    if err != nil {
        log.Fatal(err)
    }
    fmt.Print(result.Candidates[0].Content.Parts[0].Text)
}

Yapılandırma

Önce

Python

import google.generativeai as genai

model = genai.GenerativeModel(
  'gemini-3.8-flash',
    system_instruction='you are a story teller for kids under 5 years old',
    generation_config=genai.GenerationConfig(
      max_output_tokens=400,
      top_k=2,
      top_p=0.5,
      temperature=0.5,
      response_mime_type='application/json',
      stop_sequences=['\n'],
    )
)
response = model.generate_content('tell me a story in 100 words')

JavaScript

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI("GEMINI_API_KEY");
const model = genAI.getGenerativeModel({
  model: "gemini-3.8-flash",
  generationConfig: {
    candidateCount: 1,
    stopSequences: ["x"],
    maxOutputTokens: 20,
    temperature: 1.0,
  },
});

const result = await model.generateContent(
  "Tell me a story about a magic backpack.",
);
console.log(result.response.text())

Java

import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.util.Arrays;

Client client = new Client();

GenerateContentConfig config =
    GenerateContentConfig.builder()
        .systemInstruction(
            Content.fromParts(Part.fromText("you are a story teller for kids under 5 years old")))
        .maxOutputTokens(400)
        .topK(2.0f)
        .topP(0.5f)
        .temperature(0.5f)
        .responseMimeType("application/json")
        .stopSequences(Arrays.asList("\n"))
        .build();

GenerateContentResponse response =
    client.models.generateContent("gemini-3.8-flash", "tell me a story in 100 words", config);
System.out.println(response.text());

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, option.WithAPIKey("GEMINI_API_KEY"))
if err != nil {
    log.Fatal(err)
}
defer client.Close()

model := client.GenerativeModel("gemini-3.8-flash")
model.SetTemperature(0.5)
model.SetTopP(0.5)
model.SetTopK(2.0)
model.SetMaxOutputTokens(100)
model.ResponseMIMEType = "application/json"
resp, err := model.GenerateContent(ctx, genai.Text("Tell me about New York"))
if err != nil {
    log.Fatal(err)
}
printResponse(resp) // utility for printing response

Sonra

Python

Yeni SDK'daki tüm yöntemler için gerekli bağımsız değişkenler anahtar kelime bağımsız değişkenleri olarak sağlanır. Tüm isteğe bağlı girişler config argument içinde sağlanır. Yapılandırma bağımsız değişkenleri, google.genai.types ad alanında Python sözlükleri veya Config sınıfları olarak belirtilebilir. Kullanışlılık ve tutarlılık için types modülündeki tüm tanımlar pydantic sınıflarıdır.

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
  model='gemini-3.8-flash',
  contents='Tell me a story in 100 words.',
  config=types.GenerateContentConfig(
      system_instruction='you are a story teller for kids under 5 years old',
      max_output_tokens= 400,
      top_k= 2,
      top_p= 0.5,
      temperature= 0.5,
      response_mime_type= 'application/json',
      stop_sequences= ['\n'],
      seed=42,
  ),
)

JavaScript

import {GoogleGenAI} from '@google/genai';

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

const response = await ai.models.generateContent({
  model: "gemini-3.8-flash",
  contents: "Tell me a story about a magic backpack.",
  config: {
    candidateCount: 1,
    stopSequences: ["x"],
    maxOutputTokens: 20,
    temperature: 1.0,
  },
});

console.log(response.text);

Java

import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import java.util.Arrays;

Client client = new Client();

GenerateContentConfig config =
    GenerateContentConfig.builder()
        .candidateCount(1)
        .stopSequences(Arrays.asList("x"))
        .maxOutputTokens(20)
        .temperature(1.0f)
        .build();

GenerateContentResponse response =
    client.models.generateContent(
        "gemini-3.8-flash", "Tell me a story about a magic backpack.", config);
System.out.println(response.text());

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
    log.Fatal(err)
}

result, err := client.Models.GenerateContent(ctx,
    "gemini-3.8-flash",
    genai.Text("Tell me about New York"),
    &genai.GenerateContentConfig{
        Temperature:      genai.Ptr[float32](0.5),
        TopP:             genai.Ptr[float32](0.5),
        TopK:             genai.Ptr[float32](2.0),
        ResponseMIMEType: "application/json",
        StopSequences:    []string{"Yankees"},
        CandidateCount:   2,
        Seed:             genai.Ptr[int32](42),
        MaxOutputTokens:  128,
        PresencePenalty:  genai.Ptr[float32](0.5),
        FrequencyPenalty: genai.Ptr[float32](0.5),
    },
)
if err != nil {
    log.Fatal(err)
}
debugPrint(result) // utility for printing response

Güvenlik ayarları

Güvenlik ayarlarıyla yanıt oluşturma:

Önce

Python

import google.generativeai as genai

model = genai.GenerativeModel('gemini-3.8-flash')
response = model.generate_content(
    'say something bad',
    safety_settings={
        'HATE': 'BLOCK_ONLY_HIGH',
        'HARASSMENT': 'BLOCK_ONLY_HIGH',
  }
)

JavaScript

import { GoogleGenerativeAI, HarmCategory, HarmBlockThreshold } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI("GEMINI_API_KEY");
const model = genAI.getGenerativeModel({
  model: "gemini-3.8-flash",
  safetySettings: [
    {
      category: HarmCategory.HARM_CATEGORY_HARASSMENT,
      threshold: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
    },
  ],
});

const unsafePrompt =
  "I support Martians Soccer Club and I think " +
  "Jupiterians Football Club sucks! Write an ironic phrase telling " +
  "them how I feel about them.";

const result = await model.generateContent(unsafePrompt);

try {
  result.response.text();
} catch (e) {
  console.error(e);
  console.log(result.response.candidates[0].safetyRatings);
}

Java

import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.HarmBlockThreshold;
import com.google.genai.types.HarmCategory;
import com.google.genai.types.SafetySetting;
import java.util.Arrays;

Client client = new Client();

GenerateContentConfig config =
    GenerateContentConfig.builder()
        .safetySettings(
            Arrays.asList(
                SafetySetting.builder()
                    .category(HarmCategory.Known.HARM_CATEGORY_HARASSMENT)
                    .threshold(HarmBlockThreshold.Known.BLOCK_LOW_AND_ABOVE)
                    .build()))
        .build();

GenerateContentResponse response =
    client.models.generateContent("gemini-3.8-flash", "say something bad", config);
System.out.println(response.text());

Sonra

Python

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
  model='gemini-3.8-flash',
  contents='say something bad',
  config=types.GenerateContentConfig(
      safety_settings= [
          types.SafetySetting(
              category='HARM_CATEGORY_HATE_SPEECH',
              threshold='BLOCK_ONLY_HIGH'
          ),
      ]
  ),
)

JavaScript

import {GoogleGenAI} from '@google/genai';

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });
const unsafePrompt =
  "I support Martians Soccer Club and I think " +
  "Jupiterians Football Club sucks! Write an ironic phrase telling " +
  "them how I feel about them.";

const response = await ai.models.generateContent({
  model: "gemini-3.8-flash",
  contents: unsafePrompt,
  config: {
    safetySettings: [
      {
        category: "HARM_CATEGORY_HARASSMENT",
        threshold: "BLOCK_ONLY_HIGH",
      },
    ],
  },
});

console.log("Finish reason:", response.candidates[0].finishReason);
console.log("Safety ratings:", response.candidates[0].safetyRatings);

Java

import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.HarmBlockThreshold;
import com.google.genai.types.HarmCategory;
import com.google.genai.types.SafetySetting;
import java.util.Arrays;

Client client = new Client();

GenerateContentConfig config =
    GenerateContentConfig.builder()
        .safetySettings(
            Arrays.asList(
                SafetySetting.builder()
                    .category(HarmCategory.Known.HARM_CATEGORY_HATE_SPEECH)
                    .threshold(HarmBlockThreshold.Known.BLOCK_ONLY_HIGH)
                    .build()))
        .build();

GenerateContentResponse response =
    client.models.generateContent("gemini-3.8-flash", "say something bad", config);
System.out.println("Finish reason: " + response.finishReason());

Asenk.

Önce

Python

import google.generativeai as genai

model = genai.GenerativeModel('gemini-3.8-flash')
response = model.generate_content_async(
    'tell me a story in 100 words'
)

Sonra

Python

Yeni SDK'yı asyncio ile kullanmak için client.aio altındaki her yöntemin ayrı bir async uygulaması vardır.

from google import genai

client = genai.Client()

response = await client.aio.models.generate_content(
    model='gemini-3.8-flash',
    contents='Tell me a story in 300 words.'
)

Sohbet

Sohbet başlatma ve modele mesaj gönderme:

Önce

Python

import google.generativeai as genai

model = genai.GenerativeModel('gemini-3.8-flash')
chat = model.start_chat()

response = chat.send_message(
    "Tell me a story in 100 words")
response = chat.send_message(
    "What happened after that?")

JavaScript

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI("GEMINI_API_KEY");
const model = genAI.getGenerativeModel({ model: "gemini-3.8-flash" });
const chat = model.startChat({
  history: [
    {
      role: "user",
      parts: [{ text: "Hello" }],
    },
    {
      role: "model",
      parts: [{ text: "Great to meet you. What would you like to know?" }],
    },
  ],
});
let result = await chat.sendMessage("I have 2 dogs in my house.");
console.log(result.response.text());
result = await chat.sendMessage("How many paws are in my house?");
console.log(result.response.text());

Java

import com.google.genai.Chat;
import com.google.genai.Client;
import com.google.genai.types.GenerateContentResponse;

Client client = new Client();
Chat chat = client.chats.create("gemini-3.8-flash");

GenerateContentResponse response1 = chat.sendMessage("Tell me a story in 100 words");
System.out.println(response1.text());

GenerateContentResponse response2 = chat.sendMessage("What happened after that?");
System.out.println(response2.text());

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, option.WithAPIKey("GEMINI_API_KEY"))
if err != nil {
    log.Fatal(err)
}
defer client.Close()

model := client.GenerativeModel("gemini-3.8-flash")
cs := model.StartChat()

cs.History = []*genai.Content{
    {
        Parts: []genai.Part{
            genai.Text("Hello, I have 2 dogs in my house."),
        },
        Role: "user",
    },
    {
        Parts: []genai.Part{
            genai.Text("Great to meet you. What would you like to know?"),
        },
        Role: "model",
    },
}

res, err := cs.SendMessage(ctx, genai.Text("How many paws are in my house?"))
if err != nil {
    log.Fatal(err)
}
printResponse(res) // utility for printing the response

Sonra

Python

from google import genai

client = genai.Client()

chat = client.chats.create(model='gemini-3.8-flash')

response = chat.send_message(
    message='Tell me a story in 100 words')
response = chat.send_message(
    message='What happened after that?')

JavaScript

import {GoogleGenAI} from '@google/genai';

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });
const chat = ai.chats.create({
  model: "gemini-3.8-flash",
  history: [
    {
      role: "user",
      parts: [{ text: "Hello" }],
    },
    {
      role: "model",
      parts: [{ text: "Great to meet you. What would you like to know?" }],
    },
  ],
});

const response1 = await chat.sendMessage({
  message: "I have 2 dogs in my house.",
});
console.log("Chat response 1:", response1.text);

const response2 = await chat.sendMessage({
  message: "How many paws are in my house?",
});
console.log("Chat response 2:", response2.text);

Java

import com.google.genai.Chat;
import com.google.genai.Client;
import com.google.genai.types.GenerateContentResponse;

Client client = new Client();
Chat chat = client.chats.create("gemini-3.8-flash");

GenerateContentResponse response1 = chat.sendMessage("I have 2 dogs in my house.");
System.out.println("Chat response 1: " + response1.text());

GenerateContentResponse response2 = chat.sendMessage("How many paws are in my house?");
System.out.println("Chat response 2: " + response2.text());

Go

ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
    log.Fatal(err)
}

chat, err := client.Chats.Create(ctx, "gemini-3.8-flash", nil, nil)
if err != nil {
    log.Fatal(err)
}

result, err := chat.SendMessage(ctx, genai.Part{Text: "Hello, I have 2 dogs in my house."})
if err != nil {
    log.Fatal(err)
}
debugPrint(result) // utility for printing result

result, err = chat.SendMessage(ctx, genai.Part{Text: "How many paws are in my house?"})
if err != nil {
    log.Fatal(err)
}
debugPrint(result) // utility for printing result

İşlev çağırma

Önce

Python

import google.generativeai as genai
from enum import Enum

def get_current_weather(location: str) -> str:
    """Get the current whether in a given location.

    Args:
        location: required, The city and state, e.g. San Franciso, CA
        unit: celsius or fahrenheit
    """
    print(f'Called with: {location=}')
    return "23C"

model = genai.GenerativeModel(
    model_name="gemini-3.8-flash",
    tools=[get_current_weather]
)

response = model.generate_content("What is the weather in San Francisco?")
function_call = response.candidates[0].parts[0].function_call

Sonra

Python

Yeni SDK'da otomatik işlev çağrısı varsayılandır. Burada devre dışı bırakabilirsiniz.

from google import genai
from google.genai import types

client = genai.Client()

def get_current_weather(location: str) -> str:
    """Get the current whether in a given location.

    Args:
        location: required, The city and state, e.g. San Franciso, CA
        unit: celsius or fahrenheit
    """
    print(f'Called with: {location=}')
    return "23C"

response = client.models.generate_content(
  model='gemini-3.8-flash',
  contents="What is the weather like in Boston?",
  config=types.GenerateContentConfig(
      tools=[get_current_weather],
      automatic_function_calling={'disable': True},
  ),
)

function_call = response.candidates[0].content.parts[0].function_call

Otomatik işlev çağrısı

Önce

Python

Eski SDK yalnızca sohbette otomatik işlev çağrısını destekler. Yeni SDK'da bu, generate_content içindeki varsayılan davranıştır.

import google.generativeai as genai

def get_current_weather(city: str) -> str:
    return "23C"

model = genai.GenerativeModel(
    model_name="gemini-3.8-flash",
    tools=[get_current_weather]
)

chat = model.start_chat(
    enable_automatic_function_calling=True)
result = chat.send_message("What is the weather in San Francisco?")

Sonra

Python

from google import genai
from google.genai import types
client = genai.Client()

def get_current_weather(city: str) -> str:
    return "23C"

response = client.models.generate_content(
  model='gemini-3.8-flash',
  contents="What is the weather like in Boston?",
  config=types.GenerateContentConfig(
      tools=[get_current_weather]
  ),
)

Kod yürütme

Kod yürütme, modelin Python kodu oluşturmasına, bu kodu çalıştırmasına ve sonucu döndürmesine olanak tanıyan bir araçtır.

Önce

Python

import google.generativeai as genai

model = genai.GenerativeModel(
    model_name="gemini-3.8-flash",
    tools="code_execution"
)

result = model.generate_content(
  "What is the sum of the first 50 prime numbers? Generate and run code for "
  "the calculation, and make sure you get all 50.")

JavaScript

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI("GEMINI_API_KEY");
const model = genAI.getGenerativeModel({
  model: "gemini-3.8-flash",
  tools: [{ codeExecution: {} }],
});

const result = await model.generateContent(
  "What is the sum of the first 50 prime numbers? " +
    "Generate and run code for the calculation, and make sure you get " +
    "all 50.",
);

console.log(result.response.text());

Java

import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Tool;
import com.google.genai.types.ToolCodeExecution;
import java.util.Arrays;

Client client = new Client();

GenerateContentConfig config =
    GenerateContentConfig.builder()
        .tools(
            Arrays.asList(
                Tool.builder().codeExecution(ToolCodeExecution.builder().build()).build()))
        .build();

GenerateContentResponse response =
    client.models.generateContent(
        "gemini-3.8-flash",
        "What is the sum of the first 50 prime numbers? Generate and run code for "
            + "the calculation, and make sure you get all 50.",
        config);
System.out.println(response.text());

Sonra

Python

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model='gemini-3.8-flash',
    contents='What is the sum of the first 50 prime numbers? Generate and run '
            'code for the calculation, and make sure you get all 50.',
    config=types.GenerateContentConfig(
        tools=[types.Tool(code_execution=types.ToolCodeExecution)],
    ),
)

JavaScript

import {GoogleGenAI} from '@google/genai';

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

const response = await ai.models.generateContent({
  model: "gemini-3.8-flash",
  contents: `Write and execute code that calculates the sum of the first 50 prime numbers.
            Ensure that only the executable code and its resulting output are generated.`,
});

// Each part may contain text, executable code, or an execution result.
for (const part of response.candidates[0].content.parts) {
  console.log(part);
  console.log("\n");
}

console.log("-".repeat(80));
// The `.text` accessor concatenates the parts into a markdown-formatted text.
console.log("\n", response.text);

Java

import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import com.google.genai.types.Tool;
import com.google.genai.types.ToolCodeExecution;
import java.util.Arrays;

Client client = new Client();

GenerateContentConfig config =
    GenerateContentConfig.builder()
        .tools(
            Arrays.asList(
                Tool.builder().codeExecution(ToolCodeExecution.builder().build()).build()))
        .build();

GenerateContentResponse response =
    client.models.generateContent(
        "gemini-3.8-flash",
        "Write and execute code that calculates the sum of the first 50 prime numbers. "
            + "Ensure that only the executable code and its resulting output are generated.",
        config);

if (response.parts() != null) {
  for (Part part : response.parts()) {
    System.out.println(part);
  }
}
System.out.println(response.text());

Arama temellendirme

GoogleSearch (Gemini>=2.0) ve GoogleSearchRetrieval (Gemini < 2.0), Google tarafından desteklenen ve modelin temel oluşturma için herkese açık web verilerini almasına olanak tanıyan araçlardır.

Önce

Python

import google.generativeai as genai

model = genai.GenerativeModel('gemini-3.8-flash')
response = model.generate_content(
    contents="what is the Google stock price?",
    tools='google_search_retrieval'
)

Sonra

Python

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model='gemini-3.8-flash',
    contents='What is the Google stock price?',
    config=types.GenerateContentConfig(
        tools=[
            types.Tool(
                google_search=types.GoogleSearch()
            )
        ]
    )
)

JSON yanıtı

Yanıtları JSON biçiminde oluştur.

Önce

Python

response_schema belirterek ve response_mime_type="application/json" ayarlayarak kullanıcılar, modeli belirli bir yapıya uygun JSON yanıt üretmeye zorlayabilir.

import google.generativeai as genai
import typing_extensions as typing

class CountryInfo(typing.TypedDict):
    name: str
    population: int
    capital: str
    continent: str
    major_cities: list[str]
    gdp: int
    official_language: str
    total_area_sq_mi: int

model = genai.GenerativeModel(model_name="gemini-3.8-flash")
result = model.generate_content(
    "Give me information of the United States",
    generation_config=genai.GenerationConfig(
        response_mime_type="application/json",
        response_schema = CountryInfo
    ),
)

JavaScript

import { GoogleGenerativeAI, SchemaType } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI("GEMINI_API_KEY");

const schema = {
  description: "List of recipes",
  type: SchemaType.ARRAY,
  items: {
    type: SchemaType.OBJECT,
    properties: {
      recipeName: {
        type: SchemaType.STRING,
        description: "Name of the recipe",
        nullable: false,
      },
    },
    required: ["recipeName"],
  },
};

const model = genAI.getGenerativeModel({
  model: "gemini-3.8-flash",
  generationConfig: {
    responseMimeType: "application/json",
    responseSchema: schema,
  },
});

const result = await model.generateContent(
  "List a few popular cookie recipes.",
);
console.log(result.response.text());

Java

import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Schema;
import com.google.genai.types.Type;
import java.util.Arrays;
import java.util.Map;

Client client = new Client();

Schema schema =
    Schema.builder()
        .description("List of recipes")
        .type(Type.Known.ARRAY)
        .items(
            Schema.builder()
                .type(Type.Known.OBJECT)
                .properties(
                    Map.of(
                        "recipeName",
                        Schema.builder()
                            .type(Type.Known.STRING)
                            .description("Name of the recipe")
                            .build()))
                .required(Arrays.asList("recipeName"))
                .build())
        .build();

GenerateContentConfig config =
    GenerateContentConfig.builder()
        .responseMimeType("application/json")
        .responseSchema(schema)
        .build();

GenerateContentResponse response =
    client.models.generateContent(
        "gemini-3.8-flash", "List a few popular cookie recipes.", config);
System.out.println(response.text());

Sonra

Python

Yeni SDK, şemayı sağlamak için pydantic sınıflarını kullanır (genai.types.Schema veya eşdeğer dict iletebilirsiniz). SDK, mümkün olduğunda döndürülen JSON'u ayrıştırır ve sonucu response.parsed olarak döndürür. Şema olarak bir pydantic sınıfı sağladıysanız SDK, bu JSON sınıfını sınıfın bir örneğine dönüştürür.

from google import genai
from pydantic import BaseModel

client = genai.Client()

class CountryInfo(BaseModel):
    name: str
    population: int
    capital: str
    continent: str
    major_cities: list[str]
    gdp: int
    official_language: str
    total_area_sq_mi: int

response = client.models.generate_content(
    model='gemini-3.8-flash',
    contents='Give me information of the United States.',
    config={
        'response_mime_type': 'application/json',
        'response_schema': CountryInfo,
    },
)

response.parsed

JavaScript

import {GoogleGenAI} from '@google/genai';

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });
const response = await ai.models.generateContent({
  model: "gemini-3.8-flash",
  contents: "List a few popular cookie recipes.",
  config: {
    responseMimeType: "application/json",
    responseSchema: {
      type: "array",
      items: {
        type: "object",
        properties: {
          recipeName: { type: "string" },
          ingredients: { type: "array", items: { type: "string" } },
        },
        required: ["recipeName", "ingredients"],
      },
    },
  },
});
console.log(response.text);

Java

import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Schema;
import com.google.genai.types.Type;
import java.util.Arrays;
import java.util.Map;

Client client = new Client();

Schema schema =
    Schema.builder()
        .type(Type.Known.ARRAY)
        .items(
            Schema.builder()
                .type(Type.Known.OBJECT)
                .properties(
                    Map.of(
                        "recipeName", Schema.builder().type(Type.Known.STRING).build(),
                        "ingredients",
                            Schema.builder()
                                .type(Type.Known.ARRAY)
                                .items(Schema.builder().type(Type.Known.STRING).build())
                                .build()))
                .required(Arrays.asList("recipeName", "ingredients"))
                .build())
        .build();

GenerateContentConfig config =
    GenerateContentConfig.builder()
        .responseMimeType("application/json")
        .responseSchema(schema)
        .build();

GenerateContentResponse response =
    client.models.generateContent(
        "gemini-3.8-flash", "List a few popular cookie recipes.", config);
System.out.println(response.text());

Dosyalar

Yükleyin

Dosya yükleme:

Önce

Python

import requests
import pathlib
import google.generativeai as genai

# Download file
response = requests.get(
    'https://storage.googleapis.com/generativeai-downloads/data/a11.txt')
pathlib.Path('a11.txt').write_text(response.text)

file = genai.upload_file(path='a11.txt')

model = genai.GenerativeModel('gemini-3.8-flash')
response = model.generate_content([
    'Can you summarize this file:',
    my_file
])
print(response.text)

Sonra

Python

import requests
import pathlib
from google import genai

client = genai.Client()

# Download file
response = requests.get(
    'https://storage.googleapis.com/generativeai-downloads/data/a11.txt')
pathlib.Path('a11.txt').write_text(response.text)

my_file = client.files.upload(file='a11.txt')

response = client.models.generate_content(
    model='gemini-3.8-flash',
    contents=[
        'Can you summarize this file:',
        my_file
    ]
)
print(response.text)

Listeleme ve edinme

Yüklenen dosyaları listeleme ve dosya adıyla yüklenen bir dosyayı alma:

Önce

Python

import google.generativeai as genai

for file in genai.list_files():
  print(file.name)

file = genai.get_file(name=file.name)

Sonra

Python

from google import genai
client = genai.Client()

for file in client.files.list():
    print(file.name)

file = client.files.get(name=file.name)

Sil

Dosya silme:

Önce

Python

import pathlib
import google.generativeai as genai

pathlib.Path('dummy.txt').write_text(dummy)
dummy_file = genai.upload_file(path='dummy.txt')

file = genai.delete_file(name=dummy_file.name)

Sonra

Python

import pathlib
from google import genai

client = genai.Client()

pathlib.Path('dummy.txt').write_text(dummy)
dummy_file = client.files.upload(file='dummy.txt')

response = client.files.delete(name=dummy_file.name)

Bağlamı önbelleğe alma

Bağlamı önbelleğe alma, kullanıcının içeriği modele bir kez iletmesine, giriş jetonlarını önbelleğe almasına ve ardından maliyeti düşürmek için sonraki çağrılarda önbelleğe alınan jetonlara başvurmasına olanak tanır.

Önce

Python

import requests
import pathlib
import google.generativeai as genai
from google.generativeai import caching

# Download file
response = requests.get(
    'https://storage.googleapis.com/generativeai-downloads/data/a11.txt')
pathlib.Path('a11.txt').write_text(response.text)

# Upload file
document = genai.upload_file(path="a11.txt")

# Create cache
apollo_cache = caching.CachedContent.create(
    model="gemini-3.8-flash",
    system_instruction="You are an expert at analyzing transcripts.",
    contents=[document],
)

# Generate response
apollo_model = genai.GenerativeModel.from_cached_content(
    cached_content=apollo_cache
)
response = apollo_model.generate_content("Find a lighthearted moment from this transcript")

JavaScript

import { GoogleAICacheManager, GoogleAIFileManager } from "@google/generative-ai/server";
import { GoogleGenerativeAI } from "@google/generative-ai";

const cacheManager = new GoogleAICacheManager("GEMINI_API_KEY");
const fileManager = new GoogleAIFileManager("GEMINI_API_KEY");

const uploadResult = await fileManager.uploadFile("path/to/a11.txt", {
  mimeType: "text/plain",
});

const cacheResult = await cacheManager.create({
  model: "models/gemini-3.8-flash",
  contents: [
    {
      role: "user",
      parts: [
        {
          fileData: {
            fileUri: uploadResult.file.uri,
            mimeType: uploadResult.file.mimeType,
          },
        },
      ],
    },
  ],
});

console.log(cacheResult);

const genAI = new GoogleGenerativeAI("GEMINI_API_KEY");
const model = genAI.getGenerativeModelFromCachedContent(cacheResult);
const result = await model.generateContent(
  "Please summarize this transcript.",
);
console.log(result.response.text());

Java

import com.google.genai.Client;
import com.google.genai.types.CachedContent;
import com.google.genai.types.Content;
import com.google.genai.types.CreateCachedContentConfig;
import com.google.genai.types.File;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.util.Arrays;

Client client = new Client();

File uploadResult = client.files.upload("path/to/a11.txt", null);

CachedContent cacheResult =
    client.caches.create(
        "gemini-3.8-flash",
        CreateCachedContentConfig.builder()
            .contents(
                Arrays.asList(
                    Content.fromParts(
                        Part.fromUri(
                            uploadResult.uri().orElse(""),
                            uploadResult.mimeType().orElse("text/plain")))))
            .systemInstruction(
                Content.fromParts(Part.fromText("You are an expert at analyzing transcripts.")))
            .build());

GenerateContentResponse response =
    client.models.generateContent(
        "gemini-3.8-flash",
        "Please summarize this transcript.",
        GenerateContentConfig.builder().cachedContent(cacheResult.name().orElse("")).build());
System.out.println(response.text());

Sonra

Python

import requests
import pathlib
from google import genai
from google.genai import types

client = genai.Client()

# Check which models support caching.
for m in client.models.list():
  for action in m.supported_actions:
    if action == "createCachedContent":
      print(m.name)
      break

# Download file
response = requests.get(
    'https://storage.googleapis.com/generativeai-downloads/data/a11.txt')
pathlib.Path('a11.txt').write_text(response.text)

# Upload file
document = client.files.upload(file='a11.txt')

# Create cache
model='gemini-3.8-flash'
apollo_cache = client.caches.create(
      model=model,
      config={
          'contents': [document],
          'system_instruction': 'You are an expert at analyzing transcripts.',
      },
  )

# Generate response
response = client.models.generate_content(
    model=model,
    contents='Find a lighthearted moment from this transcript',
    config=types.GenerateContentConfig(
        cached_content=apollo_cache.name,
    )
)

JavaScript

import {GoogleGenAI} from '@google/genai';

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });
const filePath = path.join(media, "a11.txt");
const document = await ai.files.upload({
  file: filePath,
  config: { mimeType: "text/plain" },
});
console.log("Uploaded file name:", document.name);
const modelName = "gemini-3.8-flash";

const contents = [
  createUserContent(createPartFromUri(document.uri, document.mimeType)),
];

const cache = await ai.caches.create({
  model: modelName,
  config: {
    contents: contents,
    systemInstruction: "You are an expert analyzing transcripts.",
  },
});
console.log("Cache created:", cache);

const response = await ai.models.generateContent({
  model: modelName,
  contents: "Please summarize this transcript",
  config: { cachedContent: cache.name },
});
console.log("Response text:", response.text);

Java

import com.google.genai.Client;
import com.google.genai.types.CachedContent;
import com.google.genai.types.Content;
import com.google.genai.types.CreateCachedContentConfig;
import com.google.genai.types.File;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.util.Arrays;

Client client = new Client();

File document = client.files.upload("a11.txt", null);
String modelName = "gemini-3.8-flash";

CachedContent cache =
    client.caches.create(
        modelName,
        CreateCachedContentConfig.builder()
            .contents(
                Arrays.asList(
                    Content.fromParts(
                        Part.fromUri(
                            document.uri().orElse(""), document.mimeType().orElse("text/plain")))))
            .systemInstruction(
                Content.fromParts(Part.fromText("You are an expert analyzing transcripts.")))
            .build());

GenerateContentResponse response =
    client.models.generateContent(
        modelName,
        "Find a lighthearted moment from this transcript",
        GenerateContentConfig.builder().cachedContent(cache.name().orElse("")).build());
System.out.println(response.text());

Parça sayma

Bir istekteki jeton sayısını hesaplar.

Önce

Python

import google.generativeai as genai

model = genai.GenerativeModel('gemini-3.8-flash')
response = model.count_tokens(
    'The quick brown fox jumps over the lazy dog.')

JavaScript

 import { GoogleGenerativeAI } from "@google/generative-ai";

 const genAI = new GoogleGenerativeAI("GEMINI_API_KEY");
 const model = genAI.getGenerativeModel({
   model: "gemini-3.8-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);
 // candidatesTokenCount and totalTokenCount depend on response, may vary
 // { promptTokenCount: 11, candidatesTokenCount: 124, totalTokenCount: 135 }

Java

import com.google.genai.Client;
import com.google.genai.types.CountTokensResponse;
import com.google.genai.types.GenerateContentResponse;

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

CountTokensResponse countResult = client.models.countTokens("gemini-3.8-flash", prompt, null);
System.out.println(countResult.totalTokens().orElse(0));

GenerateContentResponse generateResult =
    client.models.generateContent("gemini-3.8-flash", prompt, null);
System.out.println(generateResult.usageMetadata());

Sonra

Python

from google import genai

client = genai.Client()

response = client.models.count_tokens(
    model='gemini-3.8-flash',
    contents='The quick brown fox jumps over the lazy dog.',
)

JavaScript

import {GoogleGenAI} from '@google/genai';

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });
const prompt = "The quick brown fox jumps over the lazy dog.";
const countTokensResponse = await ai.models.countTokens({
  model: "gemini-3.8-flash",
  contents: prompt,
});
console.log(countTokensResponse.totalTokens);

const generateResponse = await ai.models.generateContent({
  model: "gemini-3.8-flash",
  contents: prompt,
});
console.log(generateResponse.usageMetadata);

Java

import com.google.genai.Client;
import com.google.genai.types.CountTokensResponse;
import com.google.genai.types.GenerateContentResponse;

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

CountTokensResponse countTokensResponse =
    client.models.countTokens("gemini-3.8-flash", prompt, null);
System.out.println(countTokensResponse.totalTokens().orElse(0));

GenerateContentResponse generateResponse =
    client.models.generateContent("gemini-3.8-flash", prompt, null);
System.out.println(generateResponse.usageMetadata());

Resimleri oluştur

Görsel oluşturma:

Önce

Python

#pip install https://github.com/google-gemini/generative-ai-python@imagen
import google.generativeai as genai

imagen = genai.ImageGenerationModel(
    "imagen-3.0-generate-001")
gen_images = imagen.generate_images(
    prompt="Robot holding a red skateboard",
    number_of_images=1,
    safety_filter_level="block_low_and_above",
    person_generation="allow_adult",
    aspect_ratio="3:4",
)

Sonra

Python

from google import genai

client = genai.Client()

gen_images = client.models.generate_images(
    model='gemini-2.5-flash-image',
    prompt='Robot holding a red skateboard',
    config=types.GenerateImagesConfig(
        number_of_images= 1,
        safety_filter_level= "BLOCK_LOW_AND_ABOVE",
        person_generation= "ALLOW_ADULT",
        aspect_ratio= "3:4",
    )
)

for n, image in enumerate(gen_images.generated_images):
    pathlib.Path(f'{n}.png').write_bytes(
        image.image.image_bytes)

İçerik yerleştirme

İçerik yerleştirmeleri oluşturun.

Önce

Python

import google.generativeai as genai

response = genai.embed_content(
  model='models/gemini-embedding-001',
  content='Hello world'
)

JavaScript

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI("GEMINI_API_KEY");
const model = genAI.getGenerativeModel({
  model: "gemini-embedding-001",
});

const result = await model.embedContent("Hello world!");

console.log(result.embedding);

Java

import com.google.genai.Client;
import com.google.genai.types.EmbedContentResponse;

Client client = new Client();

EmbedContentResponse response =
    client.models.embedContent("gemini-embedding-001", "Hello world!", null);
System.out.println(response.embeddings());

Sonra

Python

from google import genai

client = genai.Client()

response = client.models.embed_content(
  model='gemini-embedding-001',
  contents='Hello world',
)

JavaScript

import {GoogleGenAI} from '@google/genai';

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });
const text = "Hello World!";
const result = await ai.models.embedContent({
  model: "gemini-embedding-001",
  contents: text,
  config: { outputDimensionality: 10 },
});
console.log(result.embeddings);

Java

import com.google.genai.Client;
import com.google.genai.types.EmbedContentConfig;
import com.google.genai.types.EmbedContentResponse;

Client client = new Client();
String text = "Hello World!";

EmbedContentResponse result =
    client.models.embedContent(
        "gemini-embedding-001",
        text,
        EmbedContentConfig.builder().outputDimensionality(10).build());
System.out.println(result.embeddings());