Menjalankan Gemma dengan Gemini API

Gemini API menyediakan akses yang dihosting ke Gemma sebagai API pemrograman yang dapat Anda gunakan dalam pengembangan atau pembuatan prototipe aplikasi. API ini adalah alternatif yang mudah untuk menyiapkan instance lokal Gemma dan layanan web Anda sendiri untuk menangani tugas AI generatif.

Model yang Didukung

Gemini API mendukung model Gemma 4 berikut:

  • gemma-4-31b-it
  • gemma-4-26b-a4b-it

Contoh berikut menunjukkan cara menggunakan Gemma dengan Gemini API:

Python

from google import genai

client = genai.Client()

response = client.models.generate_content(
    model="gemma-4-26b-a4b-it",
    contents="Roses are red...",
)

print(response.text)

JavaScript

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

const ai = new GoogleGenAI();

const response = await ai.models.generateContent({
  model: "gemma-4-26b-a4b-it",
  contents: "Roses are red...",
});
console.log(response.text);

REST

curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
  "contents": [{
    "parts":[{"text": "Roses are red..."}]
    }]
   }'

Mendapatkan Kunci API

Anda dapat mengakses Gemini API di banyak platform, seperti layanan seluler, web, dan cloud, serta dengan beberapa bahasa pemrograman. Untuk mengetahui informasi selengkapnya tentang paket Gemini API SDK, lihat halaman download Gemini API SDK. Untuk pengantar umum tentang Gemini API, lihat panduan memulai Gemini API.

Penalaran

Gemma 4 menggunakan "proses penalaran" internal yang mengoptimalkan penalaran multi-langkahnya, sehingga memberikan performa unggul dalam domain yang menuntut logika seperti coding algoritmik dan bukti matematika lanjutan.

Meskipun Gemma 4 secara ketat mendukung pengaktifan atau penonaktifan fitur ini, Anda dapat mengaktifkannya di API dengan menetapkan tingkat penalaran ke "high".

Contoh berikut menunjukkan cara mengaktifkan proses penalaran:

Python

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemma-4-26b-a4b-it",
    contents="What is the water formula?",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="high")
    ),
)

print(response.text)

JavaScript

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

const ai = new GoogleGenAI();

const response = await ai.models.generateContent({
  model: "gemma-4-26b-a4b-it",
  contents: "What is the water formula?",
  config: {
    thinkingConfig: {
      thinkingLevel: ThinkingLevel.HIGH,
    },
  },
});
console.log(response.text);

REST

curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
  "contents": [{
    "parts":[{"text": "What is the water formula?"}]
    }],
    "generationConfig": {
      "thinkingConfig": {
            "thinkingLevel": "high"
      }
    }
   }'

Pelajari Penalaran lebih lanjut:

Pemahaman Gambar

Model Gemma 4 dapat memproses gambar, sehingga memungkinkan banyak kasus penggunaan developer terdepan yang secara historis memerlukan model khusus domain.

Contoh berikut menunjukkan cara menggunakan input Gambar Gemma dengan Gemini API:

Python

from google import genai

client = genai.Client()

my_file = client.files.upload(file="path/to/sample.jpg")

response = client.models.generate_content(
    model="gemma-4-26b-a4b-it",
    contents=[my_file, "Caption this image."],
)

print(response.text)

JavaScript

import {
  GoogleGenAI,
  createUserContent,
  createPartFromUri,
} from "@google/genai";

const ai = new GoogleGenAI();

const myfile = await ai.files.upload({
  file: "path/to/sample.jpg",
  config: { mimeType: "image/jpeg" },
});

const response = await ai.models.generateContent({
  model: "gemma-4-26b-a4b-it",
  contents: createUserContent([
    createPartFromUri(myfile.uri, myfile.mimeType),
    "Caption this image.",
  ]),
});
console.log(response.text);
 ```

REST

IMAGE_PATH="cats-and-dogs.jpg"
MIME_TYPE=$(file -b --mime-type "${IMAGE_PATH}")
NUM_BYTES=$(wc -c < "${IMAGE_PATH}")
DISPLAY_NAME=IMAGE

tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files" \
  -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: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${IMAGE_PATH}" 2> /dev/null > file_info.json

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

# Now generate content using that file
curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"file_data":{"mime_type": "'"${MIME_TYPE}"'", "file_uri": "'"${file_uri}"'"}},
          {"text": "Caption this image."}]
        }]
      }' 2> /dev/null > response.json

cat response.json
echo

jq -r ".candidates[].content.parts[].text" response.json

Pelajari Pemahaman Gambar lebih lanjut:

Petunjuk Sistem

Anda dapat meneruskan petunjuk sistem untuk menetapkan perilaku model:

Python

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemma-4-26b-a4b-it",
    config=types.GenerateContentConfig(
        system_instruction="You are a wise Kyoto tea master. Speak calmly and poetically, using nature metaphors. Keep answers under 3 sentences."
    ),
    contents="What is the purpose of the tea ceremony?"
)
print(response.text)

JavaScript

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

const ai = new GoogleGenAI();

const response = await ai.models.generateContent({
  model: "gemma-4-26b-a4b-it",
  contents: "What is the purpose of the tea ceremony?",
  config: {
    systemInstruction: "You are a wise Kyoto tea master. Speak calmly and poetically, using nature metaphors. Keep answers under 3 sentences."
  }
});
console.log(response.text);

REST

curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
  "contents": [{
    "parts":[{"text": "What is the purpose of the tea ceremony?"}]
  }],
  "systemInstruction": {
    "parts": [{"text": "You are a wise Kyoto tea master. Speak calmly and poetically, using nature metaphors. Keep answers under 3 sentences."}]
  }
}'

Percakapan Multi-giliran

SDK menyediakan antarmuka chat yang melacak histori percakapan secara otomatis:

Python

from google import genai

client = genai.Client()
chat = client.chats.create(model="gemma-4-26b-a4b-it")

response = chat.send_message("What are the three most famous castles in Japan?")
print(response.text)

response = chat.send_message("Which one should I visit in spring for cherry blossoms?")
print(response.text)

JavaScript

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

const ai = new GoogleGenAI();
const chat = ai.chats.create({ model: "gemma-4-26b-a4b-it" });

let response = await chat.sendMessage({ message: "What are the three most famous castles in Japan?" });
console.log(response.text);

response = await chat.sendMessage({ message: "Which one should I visit in spring for cherry blossoms?" });
console.log(response.text);

REST

curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
  "contents": [
    {
      "role": "user",
      "parts": [{ "text": "What are the three most famous castles in Japan?" }]
    },
    {
      "role": "model",
      "parts": [{ "text": "Himeji Castle, Matsumoto Castle, and Kumamoto Castle are often considered the top three." }]
    },
    {
      "role": "user",
      "parts": [{ "text": "Which one should I visit in spring for cherry blossoms?" }]
    }
  ]
}'

Pemanggilan Fungsi

Tentukan alat sebagai deklarasi fungsi. Model akan memutuskan kapan harus memanggilnya:

Python

from google import genai
from google.genai import types

# Define the function declaration
get_weather = {
    "name": "get_weather",
    "description": "Get current weather for a given location.",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "City and state, e.g. 'San Francisco, CA'",
            },
        },
        "required": ["location"],
    },
}

client = genai.Client()
tools = types.Tool(function_declarations=[get_weather])
config = types.GenerateContentConfig(tools=[tools])

response = client.models.generate_content(
    model="gemma-4-26b-a4b-it",
    contents="Should I bring an umbrella to Kyoto today?",
    config=config,
)

# The model returns a function call instead of text
if response.function_calls:
    for fc in response.function_calls:
        print(f"Function to call: {fc.name}")
        print(f"ID: {fc.id}")
        print(f"Arguments: {fc.args}")
else:
    print("No function call found in the response.")
    print(response.text)

JavaScript

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

const ai = new GoogleGenAI();

const get_weather = {
    name: "get_weather",
    description: "Get current weather for a given location.",
    parameters: {
        type: "object",
        properties: {
            location: {
                type: "string",
                description: "City and state, e.g. 'San Francisco, CA'",
            },
        },
        required: ["location"],
    },
};

const response = await ai.models.generateContent({
  model: "gemma-4-26b-a4b-it",
  contents: "Should I bring an umbrella to Kyoto today?",
  config: {
    tools: [{ functionDeclarations: [get_weather] }]
  }
});

if (response.functionCalls) {
    for (const fc of response.functionCalls) {
        console.log(`Function to call: ${fc.name}`);
        console.log(`Arguments: ${JSON.stringify(fc.args)}`);
    }
} else {
    console.log("No function call found in the response.");
    console.log(response.text);
}

REST

curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
  "contents": [{
    "parts":[{"text": "Should I bring an umbrella to Kyoto today?"}]
  }],
  "tools": [{
    "functionDeclarations": [{
      "name": "get_weather",
      "description": "Get current weather for a given location.",
      "parameters": {
        "type": "OBJECT",
        "properties": {
          "location": {
            "type": "STRING",
            "description": "City and state, e.g. 'San Francisco, CA'"
          }
        },
        "required": ["location"]
      }
    }]
  }]
}'

Lakukan grounding pada respons Gemma 4 dalam data web real-time dengan Google Penelusuran:

Python

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemma-4-26b-a4b-it",
    contents="What are the dates for cherry blossom season in Tokyo this year?",
    config=types.GenerateContentConfig(
        tools=[{"google_search":{}}]
    ),
)

print(response.text)

# Access grounding metadata for citations
for chunk in response.candidates[0].grounding_metadata.grounding_chunks:
    print(f"Source: {chunk.web.title} — {chunk.web.uri}")

JavaScript

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

const ai = new GoogleGenAI();

const response = await ai.models.generateContent({
  model: "gemma-4-26b-a4b-it",
  contents: "What are the dates for cherry blossom season in Tokyo this year?",
  config: {
    tools: [{ googleSearch: {} }]
  }
});

console.log(response.text);

if (response.candidates?.[0]?.groundingMetadata?.groundingChunks) {
    for (const chunk of response.candidates[0].groundingMetadata.groundingChunks) {
        if (chunk.web) {
            console.log(`Source: ${chunk.web.title} — ${chunk.web.uri}`);
        }
    }
}

REST

curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
  "contents": [{
    "parts":[{"text": "What are the dates for cherry blossom season in Tokyo this year?"}]
  }],
  "tools": [{"googleSearch": {}}]
}'