バッチ API

Gemini Batch API は、大量のリクエストを 非同期で標準料金の 50% で処理するように設計されています。 目標の処理時間は 24 時間ですが、ほとんどの場合はこれよりはるかに短くなります。

Batch API は、データの前処理や評価の実行など、すぐにレスポンスを必要としない大規模で緊急性の低いタスクに使用します。

バッチジョブを作成する

Batch API でリクエストを送信する方法は 2 つあります。

  • インライン リクエスト: バッチ作成リクエストに直接含まれる GenerateContentRequest オブジェクトのリスト。これは、リクエストの合計サイズが 20 MB 未満の小規模なバッチに適しています。モデルから返される出力 は、inlineResponse オブジェクトのリストです。
  • 入力ファイル: 各行に完全な GenerateContentRequest オブジェクトが含まれる JSON Lines（JSONL） ファイル。この方法は、大規模なリクエストにおすすめです。モデルから返される出力 は、各行が GenerateContentResponse またはステータス オブジェクトである JSONL ファイルです。

インライン リクエスト

リクエストの数が少ない場合は、 GenerateContentRequest オブジェクトを BatchGenerateContentRequest に直接埋め込むことができます。次の例では、インライン リクエストを使用して BatchGenerateContent メソッドを呼び出します。

Python


from google import genai
from google.genai import types

client = genai.Client()

# A list of dictionaries, where each is a GenerateContentRequest
inline_requests = [
    {
        'contents': [{
            'parts': [{'text': 'Tell me a one-sentence joke.'}],
            'role': 'user'
        }]
    },
    {
        'contents': [{
            'parts': [{'text': 'Why is the sky blue?'}],
            'role': 'user'
        }]
    }
]

inline_batch_job = client.batches.create(
    model="gemini-3-flash-preview",
    src=inline_requests,
    config={
        'display_name': "inlined-requests-job-1",
    },
)

print(f"Created batch job: {inline_batch_job.name}")

JavaScript


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

const ai = new GoogleGenAI({});

const inlinedRequests = [
    {
        contents: [{
            parts: [{text: 'Tell me a one-sentence joke.'}],
            role: 'user'
        }]
    },
    {
        contents: [{
            parts: [{'text': 'Why is the sky blue?'}],
            role: 'user'
        }]
    }
]

const response = await ai.batches.create({
    model: 'gemini-3-flash-preview',
    src: inlinedRequests,
    config: {
        displayName: 'inlined-requests-job-1',
    }
});

console.log(response);

REST

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:batchGenerateContent \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-X POST \
-H "Content-Type:application/json" \
-d '{
    "batch": {
        "display_name": "my-batch-requests",
        "input_config": {
            "requests": {
                "requests": [
                    {
                        "request": {"contents": [{"parts": [{"text": "Describe the process of photosynthesis."}]}]},
                        "metadata": {
                            "key": "request-1"
                        }
                    },
                    {
                        "request": {"contents": [{"parts": [{"text": "Describe the process of photosynthesis."}]}]},
                        "metadata": {
                            "key": "request-2"
                        }
                    }
                ]
            }
        }
    }
}'

入力ファイル

リクエストのセットが大きい場合は、JSON Lines（JSONL）ファイルを用意します。このファイルの各行は、ユーザー定義のキーとリクエスト オブジェクトを含む JSON オブジェクトにする必要があります。リクエストは有効なGenerateContentRequest オブジェクトです。ユーザー定義のキーは、どの出力がどのリクエストの結果であるかを示すためにレスポンスで使用されます。たとえば、キーが request-1 として定義されているリクエストの場合、レスポンスには同じキー名のアノテーションが付けられます。

このファイルは File API を使用してアップロードされます。入力ファイルに許容される最大ファイルサイズは 2 GB です。

JSONL ファイルの例を次に示します。my-batch-requests.json という名前のファイルに保存できます。

{"key": "request-1", "request": {"contents": [{"parts": [{"text": "Describe the process of photosynthesis."}]}], "generation_config": {"temperature": 0.7}}}
{"key": "request-2", "request": {"contents": [{"parts": [{"text": "What are the main ingredients in a Margherita pizza?"}]}]}}

インライン リクエストと同様に、各リクエスト JSON で、システム指示、ツール、その他の構成などの他のパラメータを指定できます。

このファイルは、次の例に示すように、File API を 使用してアップロードできます。マルチモーダル入力を使用している場合は、JSONL ファイル内でアップロードした他のファイルを参照できます。

Python


import json
from google import genai
from google.genai import types

client = genai.Client()

# Create a sample JSONL file
with open("my-batch-requests.jsonl", "w") as f:
    requests = [
        {"key": "request-1", "request": {"contents": [{"parts": [{"text": "Describe the process of photosynthesis."}]}]}},
        {"key": "request-2", "request": {"contents": [{"parts": [{"text": "What are the main ingredients in a Margherita pizza?"}]}]}}
    ]
    for req in requests:
        f.write(json.dumps(req) + "\n")

# Upload the file to the File API
uploaded_file = client.files.upload(
    file='my-batch-requests.jsonl',
    config=types.UploadFileConfig(display_name='my-batch-requests', mime_type='jsonl')
)

print(f"Uploaded file: {uploaded_file.name}")

JavaScript


import {GoogleGenAI} from '@google/genai';
import * as fs from "fs";
import * as path from "path";
import { fileURLToPath } from 'url';

const ai = new GoogleGenAI({});
const fileName = "my-batch-requests.jsonl";

// Define the requests
const requests = [
    { "key": "request-1", "request": { "contents": [{ "parts": [{ "text": "Describe the process of photosynthesis." }] }] } },
    { "key": "request-2", "request": { "contents": [{ "parts": [{ "text": "What are the main ingredients in a Margherita pizza?" }] }] } }
];

// Construct the full path to file
const __filename = fileURLToPath(import.meta.url);
const __dirname = path.dirname(__filename);
const filePath = path.join(__dirname, fileName); // __dirname is the directory of the current script

async function writeBatchRequestsToFile(requests, filePath) {
    try {
        // Use a writable stream for efficiency, especially with larger files.
        const writeStream = fs.createWriteStream(filePath, { flags: 'w' });

        writeStream.on('error', (err) => {
            console.error(`Error writing to file ${filePath}:`, err);
        });

        for (const req of requests) {
            writeStream.write(JSON.stringify(req) + '\n');
        }

        writeStream.end();

        console.log(`Successfully wrote batch requests to ${filePath}`);

    } catch (error) {
        // This catch block is for errors that might occur before stream setup,
        // stream errors are handled by the 'error' event.
        console.error(`An unexpected error occurred:`, error);
    }
}

// Write to a file.
writeBatchRequestsToFile(requests, filePath);

// Upload the file to the File API.
const uploadedFile = await ai.files.upload({file: 'my-batch-requests.jsonl', config: {
    mimeType: 'jsonl',
}});
console.log(uploadedFile.name);

REST

tmp_batch_input_file=batch_input.tmp
echo -e '{"contents": [{"parts": [{"text": "Describe the process of photosynthesis."}]}], "generationConfig": {"temperature": 0.7}}\n{"contents": [{"parts": [{"text": "What are the main ingredients in a Margherita pizza?"}]}]}' > batch_input.tmp
MIME_TYPE=$(file -b --mime-type "${tmp_batch_input_file}")
NUM_BYTES=$(wc -c < "${tmp_batch_input_file}")
DISPLAY_NAME=BatchInput

tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files" \
-D "${tmp_header_file}" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-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: ${MIME_TYPE}" \
-H "Content-Type: application/jsonl" \
-d "{'file': {'display_name': '${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: ${NUM_BYTES}" \
-H "X-Goog-Upload-Offset: 0" \
-H "X-Goog-Upload-Command: upload, finalize" \
--data-binary "@${tmp_batch_input_file}" 2> /dev/null > file_info.json

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

次の例では、File API を使用してアップロードされた入力ファイルを使用して BatchGenerateContent メソッドを呼び出します。

Python

from google import genai

# Assumes `uploaded_file` is the file object from the previous step
client = genai.Client()
file_batch_job = client.batches.create(
    model="gemini-3-flash-preview",
    src=uploaded_file.name,
    config={
        'display_name': "file-upload-job-1",
    },
)

print(f"Created batch job: {file_batch_job.name}")

JavaScript

// Assumes `uploadedFile` is the file object from the previous step
const fileBatchJob = await ai.batches.create({
    model: 'gemini-3-flash-preview',
    src: uploadedFile.name,
    config: {
        displayName: 'file-upload-job-1',
    }
});

console.log(fileBatchJob);

REST

# Set the File ID taken from the upload response.
BATCH_INPUT_FILE='files/123456'
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:batchGenerateContent \
-X POST \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type:application/json" \
-d "{
    'batch': {
        'display_name': 'my-batch-requests',
        'input_config': {
            'file_name': '${BATCH_INPUT_FILE}'
        }
    }
}"

バッチジョブを作成すると、ジョブ名が返されます。この名前を使用して、ジョブのステータスをモニタリングし、ジョブが完了したら結果を取得します。

ジョブ名を含む出力の例を次に示します。


Created batch job from file: batches/123456789

バッチ エンべディングのサポート

Batch API を使用して Embeddings モデルとやり取りし、スループットを向上させることができます。 インライン リクエストまたは入力ファイルを使用してエンべディング バッチジョブを作成するには、batches.create_embeddings API を使用してエンべディング モデルを指定します。

Python

from google import genai

client = genai.Client()

# Creating an embeddings batch job with an input file request:
file_job = client.batches.create_embeddings(
    model="gemini-embedding-001",
    src={'file_name': uploaded_batch_requests.name},
    config={'display_name': "Input embeddings batch"},
)

# Creating an embeddings batch job with an inline request:
batch_job = client.batches.create_embeddings(
    model="gemini-embedding-001",
    # For a predefined list of requests `inlined_requests`
    src={'inlined_requests': inlined_requests},
    config={'display_name': "Inlined embeddings batch"},
)

JavaScript

// Creating an embeddings batch job with an input file request:
let fileJob;
fileJob = await client.batches.createEmbeddings({
    model: 'gemini-embedding-001',
    src: {fileName: uploadedBatchRequests.name},
    config: {displayName: 'Input embeddings batch'},
});
console.log(`Created batch job: ${fileJob.name}`);

// Creating an embeddings batch job with an inline request:
let batchJob;
batchJob = await client.batches.createEmbeddings({
    model: 'gemini-embedding-001',
    // For a predefined a list of requests `inlinedRequests`
    src: {inlinedRequests: inlinedRequests},
    config: {displayName: 'Inlined embeddings batch'},
});
console.log(`Created batch job: ${batchJob.name}`);

その他の例については、Batch API クックブック のエンベディングのセクションをご覧ください。

リクエストの構成

標準の非バッチ リクエストで使用するリクエスト構成を含めることができます。たとえば、Temperature やシステム指示を指定したり、他のモダリティを渡したりできます。次の例は、リクエストの 1 つにシステム指示を含むインライン リクエストの例を示しています。

Python

inline_requests_list = [
    {'contents': [{'parts': [{'text': 'Write a short poem about a cloud.'}]}]},
    {'contents': [{
        'parts': [{
            'text': 'Write a short poem about a cat.'
            }]
        }],
    'config': {
        'system_instruction': {'parts': [{'text': 'You are a cat. Your name is Neko.'}]}}
    }
]

JavaScript

inlineRequestsList = [
    {contents: [{parts: [{text: 'Write a short poem about a cloud.'}]}]},
    {contents: [{parts: [{text: 'Write a short poem about a cat.'}]}],
     config: {systemInstruction: {parts: [{text: 'You are a cat. Your name is Neko.'}]}}}
]

同様に、リクエストに使用するツールを指定できます。次の例 は、Google 検索ツールを有効にするリクエストを示しています。

Python

inlined_requests = [
{'contents': [{'parts': [{'text': 'Who won the euro 1998?'}]}]},
{'contents': [{'parts': [{'text': 'Who won the euro 2025?'}]}],
 'config':{'tools': [{'google_search': {}}]}}]

JavaScript

inlineRequestsList = [
    {contents: [{parts: [{text: 'Who won the euro 1998?'}]}]},
    {contents: [{parts: [{text: 'Who won the euro 2025?'}]}],
     config: {tools: [{googleSearch: {}}]}}
]

構造化された出力も指定できます。次の例は、バッチリクエストの を指定する方法を示しています。

Python

import time
from google import genai
from pydantic import BaseModel, TypeAdapter

class Recipe(BaseModel):
    recipe_name: str
    ingredients: list[str]

client = genai.Client()

# A list of dictionaries, where each is a GenerateContentRequest
inline_requests = [
    {
        'contents': [{
            'parts': [{'text': 'List a few popular cookie recipes, and include the amounts of ingredients.'}],
            'role': 'user'
        }],
        'config': {
            'response_mime_type': 'application/json',
            'response_schema': list[Recipe]
        }
    },
    {
        'contents': [{
            'parts': [{'text': 'List a few popular gluten free cookie recipes, and include the amounts of ingredients.'}],
            'role': 'user'
        }],
        'config': {
            'response_mime_type': 'application/json',
            'response_schema': list[Recipe]
        }
    }
]

inline_batch_job = client.batches.create(
    model="gemini-3-flash-preview",
    src=inline_requests,
    config={
        'display_name': "structured-output-job-1"
    },
)

# wait for the job to finish
job_name = inline_batch_job.name
print(f"Polling status for job: {job_name}")

while True:
    batch_job_inline = client.batches.get(name=job_name)
    if batch_job_inline.state.name in ('JOB_STATE_SUCCEEDED', 'JOB_STATE_FAILED', 'JOB_STATE_CANCELLED', 'JOB_STATE_EXPIRED'):
        break
    print(f"Job not finished. Current state: {batch_job_inline.state.name}. Waiting 30 seconds...")
    time.sleep(30)

print(f"Job finished with state: {batch_job_inline.state.name}")

# print the response
for i, inline_response in enumerate(batch_job_inline.dest.inlined_responses, start=1):
    print(f"\n--- Response {i} ---")

    # Check for a successful response
    if inline_response.response:
        # The .text property is a shortcut to the generated text.
        print(inline_response.response.text)

JavaScript


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

const ai = new GoogleGenAI({});

const inlinedRequests = [
    {
        contents: [{
            parts: [{text: 'List a few popular cookie recipes, and include the amounts of ingredients.'}],
            role: 'user'
        }],
        config: {
            responseMimeType: 'application/json',
            responseSchema: {
            type: Type.ARRAY,
            items: {
                type: Type.OBJECT,
                properties: {
                'recipeName': {
                    type: Type.STRING,
                    description: 'Name of the recipe',
                    nullable: false,
                },
                'ingredients': {
                    type: Type.ARRAY,
                    items: {
                    type: Type.STRING,
                    description: 'Ingredients of the recipe',
                    nullable: false,
                    },
                },
                },
                required: ['recipeName'],
            },
            },
        }
    },
    {
        contents: [{
            parts: [{text: 'List a few popular gluten free cookie recipes, and include the amounts of ingredients.'}],
            role: 'user'
        }],
        config: {
            responseMimeType: 'application/json',
            responseSchema: {
            type: Type.ARRAY,
            items: {
                type: Type.OBJECT,
                properties: {
                'recipeName': {
                    type: Type.STRING,
                    description: 'Name of the recipe',
                    nullable: false,
                },
                'ingredients': {
                    type: Type.ARRAY,
                    items: {
                    type: Type.STRING,
                    description: 'Ingredients of the recipe',
                    nullable: false,
                    },
                },
                },
                required: ['recipeName'],
            },
            },
        }
    }
]

const inlinedBatchJob = await ai.batches.create({
    model: 'gemini-3-flash-preview',
    src: inlinedRequests,
    config: {
        displayName: 'inlined-requests-job-1',
    }
});

このジョブの出力例を次に示します。

--- Response 1 ---
[
  {
    "recipe_name": "Chocolate Chip Cookies",
    "ingredients": [
      "1 cup (2 sticks) unsalted butter, softened",
      "3/4 cup granulated sugar",
      "3/4 cup packed light brown sugar",
      "1 large egg",
      "1 teaspoon vanilla extract",
      "2 1/4 cups all-purpose flour",
      "1 teaspoon baking soda",
      "1/2 teaspoon salt",
      "1 1/2 cups chocolate chips"
    ]
  },
  {
    "recipe_name": "Oatmeal Raisin Cookies",
    "ingredients": [
      "1 cup (2 sticks) unsalted butter, softened",
      "1 cup packed light brown sugar",
      "1/2 cup granulated sugar",
      "2 large eggs",
      "1 teaspoon vanilla extract",
      "1 1/2 cups all-purpose flour",
      "1 teaspoon baking soda",
      "1 teaspoon ground cinnamon",
      "1/2 teaspoon salt",
      "3 cups old-fashioned rolled oats",
      "1 cup raisins"
    ]
  },
  {
    "recipe_name": "Sugar Cookies",
    "ingredients": [
      "1 cup (2 sticks) unsalted butter, softened",
      "1 1/2 cups granulated sugar",
      "1 large egg",
      "1 teaspoon vanilla extract",
      "2 3/4 cups all-purpose flour",
      "1 teaspoon baking powder",
      "1/2 teaspoon salt"
    ]
  }
]

--- Response 2 ---
[
  {
    "recipe_name": "Gluten-Free Chocolate Chip Cookies",
    "ingredients": [
      "1 cup (2 sticks) unsalted butter, softened",
      "3/4 cup granulated sugar",
      "3/4 cup packed light brown sugar",
      "2 large eggs",
      "1 teaspoon vanilla extract",
      "2 1/4 cups gluten-free all-purpose flour blend (with xanthan gum)",
      "1 teaspoon baking soda",
      "1/2 teaspoon salt",
      "1 1/2 cups chocolate chips"
    ]
  },
  {
    "recipe_name": "Gluten-Free Peanut Butter Cookies",
    "ingredients": [
      "1 cup (250g) creamy peanut butter",
      "1/2 cup (100g) granulated sugar",
      "1/2 cup (100g) packed light brown sugar",
      "1 large egg",
      "1 teaspoon vanilla extract",
      "1/2 teaspoon baking soda",
      "1/4 teaspoon salt"
    ]
  },
  {
    "recipe_name": "Gluten-Free Oatmeal Raisin Cookies",
    "ingredients": [
      "1/2 cup (1 stick) unsalted butter, softened",
      "1/2 cup granulated sugar",
      "1/2 cup packed light brown sugar",
      "1 large egg",
      "1 teaspoon vanilla extract",
      "1 cup gluten-free all-purpose flour blend",
      "1/2 teaspoon baking soda",
      "1/2 teaspoon ground cinnamon",
      "1/4 teaspoon salt",
      "1 1/2 cups gluten-free rolled oats",
      "1/2 cup raisins"
    ]
  }
]

ジョブのステータスをモニタリングする

バッチジョブの作成時に取得したオペレーション名を使用して、ステータスをポーリングします。 バッチジョブの state フィールドに、現在のステータスが表示されます。バッチジョブは次のいずれかの状態になります。

  • JOB_STATE_PENDING: ジョブが作成され、サービスによる処理を待機しています。
  • JOB_STATE_RUNNING: ジョブは進行中です。
  • JOB_STATE_SUCCEEDED: ジョブが正常に完了しました。結果を取得できるようになりました。
  • JOB_STATE_FAILED: ジョブが失敗しました。詳細については、エラーの詳細をご確認ください。
  • JOB_STATE_CANCELLED: ユーザーがジョブをキャンセルしました。
  • JOB_STATE_EXPIRED: ジョブが 48 時間以上実行中または保留中のため、期限切れになりました。ジョブには取得する結果がありません。 ジョブを再送信するか、リクエストを小さなバッチに分割してみてください。

ジョブのステータスを定期的にポーリングして、完了を確認できます。

Python

import time
from google import genai

client = genai.Client()

# Use the name of the job you want to check
# e.g., inline_batch_job.name from the previous step
job_name = "YOUR_BATCH_JOB_NAME"  # (e.g. 'batches/your-batch-id')
batch_job = client.batches.get(name=job_name)

completed_states = set([
    'JOB_STATE_SUCCEEDED',
    'JOB_STATE_FAILED',
    'JOB_STATE_CANCELLED',
    'JOB_STATE_EXPIRED',
])

print(f"Polling status for job: {job_name}")
batch_job = client.batches.get(name=job_name) # Initial get
while batch_job.state.name not in completed_states:
  print(f"Current state: {batch_job.state.name}")
  time.sleep(30) # Wait for 30 seconds before polling again
  batch_job = client.batches.get(name=job_name)

print(f"Job finished with state: {batch_job.state.name}")
if batch_job.state.name == 'JOB_STATE_FAILED':
    print(f"Error: {batch_job.error}")

JavaScript

// Use the name of the job you want to check
// e.g., inlinedBatchJob.name from the previous step
let batchJob;
const completedStates = new Set([
    'JOB_STATE_SUCCEEDED',
    'JOB_STATE_FAILED',
    'JOB_STATE_CANCELLED',
    'JOB_STATE_EXPIRED',
]);

try {
    batchJob = await ai.batches.get({name: inlinedBatchJob.name});
    while (!completedStates.has(batchJob.state)) {
        console.log(`Current state: ${batchJob.state}`);
        // Wait for 30 seconds before polling again
        await new Promise(resolve => setTimeout(resolve, 30000));
        batchJob = await client.batches.get({ name: batchJob.name });
    }
    console.log(`Job finished with state: ${batchJob.state}`);
    if (batchJob.state === 'JOB_STATE_FAILED') {
        // The exact structure of `error` might vary depending on the SDK
        // This assumes `error` is an object with a `message` property.
        console.error(`Error: ${batchJob.state}`);
    }
} catch (error) {
    console.error(`An error occurred while polling job ${batchJob.name}:`, error);
}

結果を取得する

ジョブのステータスがバッチジョブの成功を示したら、response フィールドで結果を確認できます。

Python

import json
from google import genai

client = genai.Client()

# Use the name of the job you want to check
# e.g., inline_batch_job.name from the previous step
job_name = "YOUR_BATCH_JOB_NAME"
batch_job = client.batches.get(name=job_name)

if batch_job.state.name == 'JOB_STATE_SUCCEEDED':

    # If batch job was created with a file
    if batch_job.dest and batch_job.dest.file_name:
        # Results are in a file
        result_file_name = batch_job.dest.file_name
        print(f"Results are in file: {result_file_name}")

        print("Downloading result file content...")
        file_content = client.files.download(file=result_file_name)
        # Process file_content (bytes) as needed
        print(file_content.decode('utf-8'))

    # If batch job was created with inline request
    # (for embeddings, use batch_job.dest.inlined_embed_content_responses)
    elif batch_job.dest and batch_job.dest.inlined_responses:
        # Results are inline
        print("Results are inline:")
        for i, inline_response in enumerate(batch_job.dest.inlined_responses):
            print(f"Response {i+1}:")
            if inline_response.response:
                # Accessing response, structure may vary.
                try:
                    print(inline_response.response.text)
                except AttributeError:
                    print(inline_response.response) # Fallback
            elif inline_response.error:
                print(f"Error: {inline_response.error}")
    else:
        print("No results found (neither file nor inline).")
else:
    print(f"Job did not succeed. Final state: {batch_job.state.name}")
    if batch_job.error:
        print(f"Error: {batch_job.error}")

JavaScript

// Use the name of the job you want to check
// e.g., inlinedBatchJob.name from the previous step
const jobName = "YOUR_BATCH_JOB_NAME";

try {
    const batchJob = await ai.batches.get({ name: jobName });

    if (batchJob.state === 'JOB_STATE_SUCCEEDED') {
        console.log('Found completed batch:', batchJob.displayName);
        console.log(batchJob);

        // If batch job was created with a file destination
        if (batchJob.dest?.fileName) {
            const resultFileName = batchJob.dest.fileName;
            console.log(`Results are in file: ${resultFileName}`);

            console.log("Downloading result file content...");
            const fileContentBuffer = await ai.files.download({ file: resultFileName });

            // Process fileContentBuffer (Buffer) as needed
            console.log(fileContentBuffer.toString('utf-8'));
        }

        // If batch job was created with inline responses
        else if (batchJob.dest?.inlinedResponses) {
            console.log("Results are inline:");
            for (let i = 0; i < batchJob.dest.inlinedResponses.length; i++) {
                const inlineResponse = batchJob.dest.inlinedResponses[i];
                console.log(`Response ${i + 1}:`);
                if (inlineResponse.response) {
                    // Accessing response, structure may vary.
                    if (inlineResponse.response.text !== undefined) {
                        console.log(inlineResponse.response.text);
                    } else {
                        console.log(inlineResponse.response); // Fallback
                    }
                } else if (inlineResponse.error) {
                    console.error(`Error: ${inlineResponse.error}`);
                }
            }
        }

        // If batch job was an embedding batch with inline responses
        else if (batchJob.dest?.inlinedEmbedContentResponses) {
            console.log("Embedding results found inline:");
            for (let i = 0; i < batchJob.dest.inlinedEmbedContentResponses.length; i++) {
                const inlineResponse = batchJob.dest.inlinedEmbedContentResponses[i];
                console.log(`Response ${i + 1}:`);
                if (inlineResponse.response) {
                    console.log(inlineResponse.response);
                } else if (inlineResponse.error) {
                    console.error(`Error: ${inlineResponse.error}`);
                }
            }
        } else {
            console.log("No results found (neither file nor inline).");
        }
    } else {
        console.log(`Job did not succeed. Final state: ${batchJob.state}`);
        if (batchJob.error) {
            console.error(`Error: ${typeof batchJob.error === 'string' ? batchJob.error : batchJob.error.message || JSON.stringify(batchJob.error)}`);
        }
    }
} catch (error) {
    console.error(`An error occurred while processing job ${jobName}:`, error);
}

REST

BATCH_NAME="batches/123456" # Your batch job name

curl https://generativelanguage.googleapis.com/v1beta/$BATCH_NAME \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type:application/json" 2> /dev/null > batch_status.json

if jq -r '.done' batch_status.json | grep -q "false"; then
    echo "Batch has not finished processing"
fi

batch_state=$(jq -r '.metadata.state' batch_status.json)
if [[ $batch_state = "JOB_STATE_SUCCEEDED" ]]; then
    if [[ $(jq '.response | has("inlinedResponses")' batch_status.json) = "true" ]]; then
        jq -r '.response.inlinedResponses' batch_status.json
        exit
    fi
    responses_file_name=$(jq -r '.response.responsesFile' batch_status.json)
    curl https://generativelanguage.googleapis.com/download/v1beta/$responses_file_name:download?alt=media \
    -H "x-goog-api-key: $GEMINI_API_KEY" 2> /dev/null
elif [[ $batch_state = "JOB_STATE_FAILED" ]]; then
    jq '.error' batch_status.json
elif [[ $batch_state == "JOB_STATE_CANCELLED" ]]; then
    echo "Batch was cancelled by the user"
elif [[ $batch_state == "JOB_STATE_EXPIRED" ]]; then
    echo "Batch expired after 48 hours"
fi

バッチジョブを一覧表示する

最近のバッチジョブを一覧表示できます。

Python

batch_jobs = client.batches.list()

# Optional query config:
# batch_jobs = client.batches.list(config={'page_size': 5})

for batch_job in batch_jobs:
    print(batch_job)

JavaScript

const batchJobs = await ai.batches.list();

// Optional query config:
// const batchJobs = await ai.batches.list({config: {'pageSize': 5}});

for await (const batchJob of batchJobs) {
    console.log(batchJob);
}

REST

curl https://generativelanguage.googleapis.com/v1beta/batches \
-H "x-goog-api-key: $GEMINI_API_KEY"

バッチジョブをキャンセルする

実行中のバッチジョブは、名前を使用してキャンセルできます。ジョブがキャンセルされると、新しいリクエストの処理が停止します。

Python

client.batches.cancel(name=batch_job_to_cancel.name)

JavaScript

await ai.batches.cancel({name: batchJobToCancel.name});

REST

BATCH_NAME="batches/123456" # Your batch job name

# Cancel the batch
curl https://generativelanguage.googleapis.com/v1beta/$BATCH_NAME:cancel \
-H "x-goog-api-key: $GEMINI_API_KEY" \

# Confirm that the status of the batch after cancellation is JOB_STATE_CANCELLED
curl https://generativelanguage.googleapis.com/v1beta/$BATCH_NAME \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type:application/json" 2> /dev/null | jq -r '.metadata.state'

バッチジョブを削除する

既存のバッチジョブは、名前を使用して削除できます。ジョブが削除されると、新しいリクエストの処理が停止し、バッチジョブのリストから削除されます。

Python

client.batches.delete(name=batch_job_to_delete.name)

JavaScript

await ai.batches.delete({name: batchJobToDelete.name});

REST

BATCH_NAME="batches/123456" # Your batch job name

# Delete the batch job
curl https://generativelanguage.googleapis.com/v1beta/$BATCH_NAME:delete \
-H "x-goog-api-key: $GEMINI_API_KEY"

画像をバッチで生成する

Gemini Nano Banana を使用していて、大量の画像を生成する必要がある場合は、Batch API を使用してレート上限を引き上げることができます。ただし、処理時間は最大 24 時間になります。

リクエストの小規模なバッチ（20 MB 未満）にはインライン リクエストを使用し、大規模なバッチ（画像生成におすすめ）には JSONL 入力ファイルを使用できます。

Python

import json
import time
import base64
from google import genai
from google.genai import types
from PIL import Image

client = genai.Client()

# 1. Create and upload file
file_name = "my-batch-image-requests.jsonl"
with open(file_name, "w") as f:
    requests = [
        {"key": "request-1", "request": {"contents": [{"parts": [{"text": "A big letter A surrounded by animals starting with the A letter"}]}], "generation_config": {"responseModalities": ["TEXT", "IMAGE"]}}},
        {"key": "request-2", "request": {"contents": [{"parts": [{"text": "A big letter B surrounded by animals starting with the B letter"}]}], "generation_config": {"responseModalities": ["TEXT", "IMAGE"]}}}
    ]
    for req in requests:
        f.write(json.dumps(req) + "\n")

uploaded_file = client.files.upload(
    file=file_name,
    config=types.UploadFileConfig(display_name='my-batch-image-requests', mime_type='jsonl')
)
print(f"Uploaded file: {uploaded_file.name}")

# 2. Create batch job
file_batch_job = client.batches.create(
    model="gemini-3-pro-image-preview",
    src=uploaded_file.name,
    config={
        'display_name': "file-image-upload-job-1",
    },
)
print(f"Created batch job: {file_batch_job.name}")

# 3. Monitor job status
job_name = file_batch_job.name
print(f"Polling status for job: {job_name}")

completed_states = set([
    'JOB_STATE_SUCCEEDED',
    'JOB_STATE_FAILED',
    'JOB_STATE_CANCELLED',
    'JOB_STATE_EXPIRED',
])

batch_job = client.batches.get(name=job_name) # Initial get
while batch_job.state.name not in completed_states:
  print(f"Current state: {batch_job.state.name}")
  time.sleep(10) # Wait for 10 seconds before polling again
  batch_job = client.batches.get(name=job_name)

print(f"Job finished with state: {batch_job.state.name}")

# 4. Retrieve results
if batch_job.state.name == 'JOB_STATE_SUCCEEDED':
    result_file_name = batch_job.dest.file_name
    print(f"Results are in file: {result_file_name}")
    print("Downloading result file content...")
    file_content_bytes = client.files.download(file=result_file_name)
    file_content = file_content_bytes.decode('utf-8')
    # The result file is also a JSONL file. Parse and print each line.
    for line in file_content.splitlines():
      if line:
        parsed_response = json.loads(line)
        if 'response' in parsed_response and parsed_response['response']:
            for part in parsed_response['response']['candidates'][0]['content']['parts']:
              if part.get('text'):
                print(part['text'])
              elif part.get('inlineData'):
                print(f"Image mime type: {part['inlineData']['mimeType']}")
                data = base64.b64decode(part['inlineData']['data'])
        elif 'error' in parsed_response:
            print(f"Error: {parsed_response['error']}")
elif batch_job.state.name == 'JOB_STATE_FAILED':
    print(f"Error: {batch_job.error}")

JavaScript

import {GoogleGenAI} from '@google/genai';
import * as fs from "fs";
import * as path from "path";
import { fileURLToPath } from 'url';

const ai = new GoogleGenAI({});

async function run() {
    // 1. Create and upload file
    const fileName = "my-batch-image-requests.jsonl";
    const requests = [
        { "key": "request-1", "request": { "contents": [{ "parts": [{ "text": "A big letter A surrounded by animals starting with the A letter" }] }], "generation_config": {"responseModalities": ["TEXT", "IMAGE"]} } },
        { "key": "request-2", "request": { "contents": [{ "parts": [{ "text": "A big letter B surrounded by animals starting with the B letter" }] }], "generation_config": {"responseModalities": ["TEXT", "IMAGE"]} } }
    ];
    const __filename = fileURLToPath(import.meta.url);
    const __dirname = path.dirname(__filename);
    const filePath = path.join(__dirname, fileName);

    try {
        const writeStream = fs.createWriteStream(filePath, { flags: 'w' });
        for (const req of requests) {
            writeStream.write(JSON.stringify(req) + '\n');
        }
        writeStream.end();
        console.log(`Successfully wrote batch requests to ${filePath}`);
    } catch (error) {
        console.error(`An unexpected error occurred writing file:`, error);
        return;
    }

    const uploadedFile = await ai.files.upload({file: fileName, config: { mimeType: 'jsonl' }});
    console.log(`Uploaded file: ${uploadedFile.name}`);

    // 2. Create batch job
    const fileBatchJob = await ai.batches.create({
        model: 'gemini-3-pro-image-preview',
        src: uploadedFile.name,
        config: {
            displayName: 'file-image-upload-job-1',
        }
    });
    console.log(fileBatchJob);

    // 3. Monitor job status
    let batchJob;
    const completedStates = new Set([
        'JOB_STATE_SUCCEEDED',
        'JOB_STATE_FAILED',
        'JOB_STATE_CANCELLED',
        'JOB_STATE_EXPIRED',
    ]);

    try {
        batchJob = await ai.batches.get({name: fileBatchJob.name});
        while (!completedStates.has(batchJob.state)) {
            console.log(`Current state: ${batchJob.state}`);
            // Wait for 10 seconds before polling again
            await new Promise(resolve => setTimeout(resolve, 10000));
            batchJob = await ai.batches.get({ name: batchJob.name });
        }
        console.log(`Job finished with state: ${batchJob.state}`);
    } catch (error) {
        console.error(`An error occurred while polling job ${fileBatchJob.name}:`, error);
        return;
    }

    // 4. Retrieve results
    if (batchJob.state === 'JOB_STATE_SUCCEEDED') {
        if (batchJob.dest?.fileName) {
            const resultFileName = batchJob.dest.fileName;
            console.log(`Results are in file: ${resultFileName}`);
            console.log("Downloading result file content...");
            const fileContentBuffer = await ai.files.download({ file: resultFileName });
            const fileContent = fileContentBuffer.toString('utf-8');
            for (const line of fileContent.split('\n')) {
                if (line) {
                    const parsedResponse = JSON.parse(line);
                    if (parsedResponse.response) {
                        for (const part of parsedResponse.response.candidates[0].content.parts) {
                            if (part.text) {
                                console.log(part.text);
                            } else if (part.inlineData) {
                                console.log(`Image mime type: ${part.inlineData.mimeType}`);
                            }
                        }
                    } else if (parsedResponse.error) {
                        console.error(`Error: ${parsedResponse.error}`);
                    }
                }
            }
        } else {
            console.log("No result file found.");
        }
    } else if (batchJob.state === 'JOB_STATE_FAILED') {
         console.error(`Error: ${typeof batchJob.error === 'string' ? batchJob.error : batchJob.error.message || JSON.stringify(batchJob.error)}`);
    }
}
run();

REST

# 1. Create and upload file
echo '{"key": "request-1", "request": {"contents": [{"parts": [{"text": "A big letter A surrounded by animals starting with the A letter"}]}], "generation_config": {"responseModalities": ["TEXT", "IMAGE"]}}}' > my-batch-image-requests.jsonl
echo '{"key": "request-2", "request": {"contents": [{"parts": [{"text": "A big letter B surrounded by animals starting with the B letter"}]}], "generation_config": {"responseModalities": ["TEXT", "IMAGE"]}}}' >> my-batch-image-requests.jsonl

# Follow File API guide to upload: https://ai.google.dev/gemini-api/docs/files#upload_a_file
# This example assumes you have uploaded the file and set BATCH_INPUT_FILE to its name (e.g., files/abcdef123)
BATCH_INPUT_FILE="files/your-uploaded-file-name"

# 2. Create batch job
printf -v request_data '{
    "batch": {
        "display_name": "my-batch-file-image-requests",
        "input_config": { "file_name": "%s" }
    }
}' "$BATCH_INPUT_FILE"
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:batchGenerateContent \
  -X POST \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type:application/json" \
  -d "$request_data" > created_batch.json

BATCH_NAME=$(jq -r '.name' created_batch.json)
echo "Created batch job: $BATCH_NAME"

# 3. Poll job status until completion by repeating the following command:
curl https://generativelanguage.googleapis.com/v1beta/$BATCH_NAME \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type:application/json" > batch_status.json

echo "Current status:"
jq '.' batch_status.json

# 4. If state is JOB_STATE_SUCCEEDED, download results file
batch_state=$(jq -r '.state' batch_status.json)
if [[ $batch_state = "JOB_STATE_SUCCEEDED" ]]; then
    responses_file_name=$(jq -r '.dest.fileName' batch_status.json)
    echo "Job succeeded. Downloading results from $responses_file_name..."
    curl https://generativelanguage.googleapis.com/download/v1beta/$responses_file_name:download?alt=media \
      -H "x-goog-api-key: $GEMINI_API_KEY" > batch_results.jsonl
    echo "Results saved to batch_results.jsonl"
fi

詳細な技術情報

  • サポートされているモデル: Batch API は、さまざまな Gemini モデルをサポートしています。 各モデルの Batch API のサポート については、モデルのページをご覧ください。Batch API でサポートされているモダリティは、インタラクティブ（非バッチ）API でサポートされているモダリティと同じです。
  • 料金: Batch API の使用料金は、同等のモデルの標準インタラクティブ API 料金の 50% です。詳細については、料金のページ をご覧ください。この機能のレート上限について詳しくは、レート上限のページ をご覧ください。
  • サービスレベル目標（SLO）: バッチジョブは、24 時間以内の処理時間で完了するように設計されています。ジョブのサイズと現在のシステム負荷によっては、多くのジョブがはるかに早く完了する場合があります。
  • キャッシュ: コンテキスト キャッシュが有効になっています バッチ リクエスト。バッチ内のリクエストがキャッシュ ヒットした場合、キャッシュされたトークンの料金は、非バッチ API トラフィックと同じになります。

ベスト プラクティス

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