使用 Gemini API 執行 Gemma¶

Models Gemma 4 Uses Gemini API

Gemini API 提供託管的 Gemma 存取，作為可在應用程式開發或原型設計中使用的程式化 API。此 API 是自行架設 Gemma 本機執行個體與處理生成式 AI 任務的 Web 服務的便利替代方案。

支援的模型¶

Gemini API 託管 Gemma 4 中最大的兩個模型：

模型	識別碼
Gemma 4 31B	`gemma-4-31b-it`
Gemma 4 26B A4B	`gemma-4-26b-a4b-it`

較小的尺寸沒有託管——請使用 Ollama、 llama.cpp 或 LiteRT-LM 自行執行。本頁的選取器提供 API 所服務的兩種尺寸，下方的每個範例都遵循它。

PythonJavaScriptREST

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)

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);

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..."}]
    }]
   }'

取得 API 金鑰

Important

您必須取得 API 金鑰才能使用 Gemini API，可從 Google AI Studio 應用程式取得。

您可以在許多平台上存取 Gemini API，例如行動裝置、網頁與雲端服務，並支援多種程式語言。關於 Gemini API SDK 套件的更多資訊，請參閱 Gemini API SDK 下載頁面。關於 Gemini API 的一般介紹，請參閱 Gemini API 入門指南。

思考¶

Gemma 4 運用內部的「思考流程」來最佳化其多步推理，在演算法程式設計與高等數學證明等邏輯要求嚴苛的領域提供卓越的效能。

雖然 Gemma 4 僅支援開啟或關閉此功能，但您可以透過將思考等級設為 "high"（啟用）或 "minimal"（停用）來使用 API 控制它。

以下範例示範如何啟動思考流程：

PythonJavaScriptREST

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)

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);

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"
      }
    }
   }'

深入了解思考：

Gemini API 思考（一般介紹）
Gemma 思考（Gemma 專屬功能）

圖像理解¶

Gemma 4 模型可以處理圖像，為開發者開啟了許多前沿的使用案例，這些案例過去需要特定領域的模型。

以下範例示範如何在 Gemini API 中使用 Gemma 圖像輸入：

PythonJavaScriptREST

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)

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);

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

深入了解圖像理解：

Gemini API 圖像理解（一般介紹）
Gemma 圖像理解（Gemma 專屬功能）

系統指示¶

您可以傳入系統指示來設定模型的行為：

PythonJavaScriptREST

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)

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);

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."}]
  }
}'

多輪對話¶

SDK 提供了自動追蹤對話歷史的聊天介面：

PythonJavaScriptREST

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)

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);

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?" }]
    }
  ]
}'

函數呼叫¶

將工具定義為函數宣告。模型會決定何時呼叫它們：

PythonJavaScriptREST

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)

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);
}

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"]
      }
    }]
  }]
}'

Google 搜尋¶

以 Google 搜尋的即時網路資料為 Gemma 4 回應接地：

PythonJavaScriptREST

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}")

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}`);
        }
    }
}

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": {}}]
}'