自 2025 年 4 月 29 日起，Gemini 1.5 Pro 和 Gemini 1.5 Flash 模型將無法用於先前未使用這些模型的專案，包括新專案。詳情請參閱「模型版本和生命週期」。

本頁面由 Cloud Translation API 翻譯而成。

取得 Gemini 的批次預測結果

批次預測功能可讓您在單一批次要求中傳送大量多模態提示。

如要進一步瞭解批次工作流程和如何格式化輸入資料，請參閱「取得 Gemini 的批次預測結果」。

支援的模型

語法範例

下列範例說明如何使用 curl 指令，傳送批次預測 API 要求。這個範例專門說明 BigQuery 儲存空間。

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/batchPredictionJobs \
  -d '{
      "displayName": "...",
      "model": "publishers/google/models/${MODEL_ID}",
      "inputConfig": {
        "instancesFormat": "bigquery",
        "bigquerySource": {
          "inputUri" : "..."
        }
      },
      "outputConfig": {
        "predictionsFormat": "bigquery",
        "bigqueryDestination": {
          "outputUri": "..."
        }
      }
  }'

參數

如需實作詳情，請參閱範例。

要求主體

參數
`displayName`	您為工作選擇的名稱。
`model`	用於批次預測的模型。
`inputConfig`	資料格式。Gemini 批次預測支援 Cloud Storage 和 BigQuery 輸入來源。
`outputConfig`	輸出設定，決定模型輸出位置。支援 Cloud Storage 和 BigQuery 輸出位置。

`inputConfig`

參數
`instancesFormat`	提示輸入格式。Cloud Storage 請使用 `jsonl`，BigQuery 則使用 `bigquery`。
`gcsSource.uris`	輸入來源 URI。這是 JSONL 檔案的 Cloud Storage 位置，格式為 `gs://bucketname/path/to/file.jsonl`。
`bigquerySource.inputUri`	輸入來源 URI。這是 BigQuery 資料表 URI，格式為 `bq://project_id.dataset.table`。輸入 BigQuery 資料集的地區必須與 Vertex AI 批次預測工作相同。

`outputConfig`

參數
`predictionsFormat`	預測結果的輸出格式。使用 `bigquery`。
`gcsDestination.outputUriPrefix`	Cloud Storage 值區和目錄位置，格式為 `gs://mybucket/path/to/output`。
`bigqueryDestination.outputUri`	目標輸出資料表的 BigQuery URI，格式為 `bq://project_id.dataset.table`。如果資料表不存在，系統就會為您建立。輸出 BigQuery 資料集的區域必須與 Vertex AI 批次預測工作相同。

範例

要求批次回應

對多模態模型提出批次要求時，適用的輸入來源包括 Cloud Storage 和 BigQuery 儲存空間。詳情請參閱下列資源：

視您提交的輸入項目數量而定，批次生成工作可能需要一些時間才能完成。

REST

如要建立批次預測工作，請使用 projects.locations.batchPredictionJobs.create 方法。

Cloud Storage 輸入

使用任何要求資料之前，請先替換以下項目：

LOCATION：支援 Gemini 模型的區域。
PROJECT_ID：您的專案 ID。
MODEL_PATH：發布者模型名稱，例如 publishers/google/models/gemini-2.5-flash；或微調端點名稱，例如 projects/PROJECT_ID/locations/LOCATION/models/MODEL_ID，其中 MODEL_ID 是微調模型的模型 ID。
INPUT_URI：JSONL 批次預測輸入內容的 Cloud Storage 位置，例如 gs://bucketname/path/to/file.jsonl。
OUTPUT_FORMAT：如要輸出至 Cloud Storage bucket，請指定 jsonl。
DESTINATION：如果是 BigQuery，請指定 bigqueryDestination。如果是 Cloud Storage，請指定 gcsDestination。
OUTPUT_URI_FIELD_NAME：如果是 BigQuery，請指定 outputUri。如果是 Cloud Storage，請指定 outputUriPrefix。
OUTPUT_URI：如果是 BigQuery，請指定資料表位置，例如 bq://myproject.mydataset.output_result。輸出 BigQuery 資料集的區域必須與 Vertex AI 批次預測工作相同。如果是 Cloud Storage，請指定值區和目錄位置，例如 gs://mybucket/path/to/output。

JSON 要求主體：

{
  "displayName": "my-cloud-storage-batch-prediction-job",
  "model": "MODEL_PATH",
  "inputConfig": {
    "instancesFormat": "jsonl",
    "gcsSource": {
      "uris" : "INPUT_URI"
    }
  },
  "outputConfig": {
    "predictionsFormat": "OUTPUT_FORMAT",
    "DESTINATION": {
      "OUTPUT_URI_FIELD_NAME": "OUTPUT_URI"
    }
  }
}

如要傳送要求，請選擇以下其中一個選項：

curl

注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI，或使用 Cloud Shell，自動登入 gcloud CLI。您可以執行 gcloud auth list 查看目前有效的帳戶。

將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs"

PowerShell

注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI。您可以執行 gcloud auth list 查看目前有效的帳戶。

將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs" | Select-Object -Expand Content

您應該會收到類似如下的 JSON 回應。

回應

{
  "name": "projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs/BATCH_JOB_ID",
  "displayName": "my-cloud-storage-batch-prediction-job",
  "model": "publishers/google/models/gemini-2.5-flash",
  "inputConfig": {
    "instancesFormat": "jsonl",
    "gcsSource": {
      "uris": [
        "INPUT_URI"
      ]
    }
  },
  "outputConfig": {
    "predictionsFormat": "OUTPUT_FORMAT",
    "DESTINATION": {
      "OUTPUT_URI_FIELD_NAME": "OUTPUT_URI"
    }
  },
  "state": "JOB_STATE_PENDING",
  "createTime": "2024-10-16T19:33:59.153782Z",
  "updateTime": "2024-10-16T19:33:59.153782Z",
  "modelVersionId": "1"
}

BigQuery 輸入

使用任何要求資料之前，請先替換以下項目：

LOCATION：支援 Gemini 模型的區域。
PROJECT_ID：您的專案 ID。
MODEL_PATH：發布者模型名稱，例如 publishers/google/models/gemini-2.0-flash-001；或微調端點名稱，例如 projects/PROJECT_ID/locations/LOCATION/models/MODEL_ID，其中 MODEL_ID 是微調模型的模型 ID。
INPUT_URI：批次預測輸入內容所在的 BigQuery 資料表，例如 bq://myproject.mydataset.input_table。資料集必須與批次預測作業位於同一區域。不支援多區域資料集。
OUTPUT_FORMAT：如要輸出至 BigQuery 資料表，請指定 bigquery。如要輸出至 Cloud Storage 值區，請指定 jsonl。
DESTINATION：如果是 BigQuery，請指定 bigqueryDestination。如果是 Cloud Storage，請指定 gcsDestination。
OUTPUT_URI_FIELD_NAME：如果是 BigQuery，請指定 outputUri。如果是 Cloud Storage，請指定 outputUriPrefix。
OUTPUT_URI：如果是 BigQuery，請指定資料表位置，例如 bq://myproject.mydataset.output_result。輸出 BigQuery 資料集的區域必須與 Vertex AI 批次預測工作相同。如果是 Cloud Storage，請指定值區和目錄位置，例如 gs://mybucket/path/to/output。

JSON 要求主體：

{
  "displayName": "my-bigquery-batch-prediction-job",
  "model": "MODEL_PATH",
  "inputConfig": {
    "instancesFormat": "bigquery",
    "bigquerySource":{
      "inputUri" : "INPUT_URI"
    }
  },
  "outputConfig": {
    "predictionsFormat": "OUTPUT_FORMAT",
    "DESTINATION": {
      "OUTPUT_URI_FIELD_NAME": "OUTPUT_URI"
    }
  }
}

如要傳送要求，請選擇以下其中一個選項：

curl

將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs"

PowerShell

注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI。您可以執行 gcloud auth list 查看目前有效的帳戶。

將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs" | Select-Object -Expand Content

您應該會收到類似如下的 JSON 回應。

回應

{
  "name": "projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs/BATCH_JOB_ID",
  "displayName": "my-bigquery-batch-prediction-job",
  "model": "publishers/google/models/gemini-2.5-flash",
  "inputConfig": {
    "instancesFormat": "bigquery",
    "bigquerySource": {
      "inputUri" : "INPUT_URI"
    }
  },
  "outputConfig": {
    "predictionsFormat": "OUTPUT_FORMAT",
    "DESTINATION": {
      "OUTPUT_URI_FIELD_NAME": "OUTPUT_URI"
    }
  },
  "state": "JOB_STATE_PENDING",
  "createTime": "2024-10-16T19:33:59.153782Z",
  "updateTime": "2024-10-16T19:33:59.153782Z",
  "modelVersionId": "1"
}

回應會包含批次作業的專屬 ID。您可以使用 BATCH_JOB_ID 輪詢批次作業的狀態。詳情請參閱「監控工作狀態」。注意：系統不支援自訂服務帳戶、即時進度、CMEK 和 VPCSC 報表。

Python

安裝

pip install --upgrade google-genai

詳情請參閱 SDK 參考說明文件。

設定環境變數，透過 Vertex AI 使用 Gen AI SDK：

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

Cloud Storage 輸入

import time

from google import genai
from google.genai.types import CreateBatchJobConfig, JobState, HttpOptions

client = genai.Client(http_options=HttpOptions(api_version="v1"))
# TODO(developer): Update and un-comment below line
# output_uri = "gs://your-bucket/your-prefix"

# See the documentation: https://googleapis.github.io/python-genai/genai.html#genai.batches.Batches.create
job = client.batches.create(
    # To use a tuned model, set the model param to your tuned model using the following format:
    # model="projects/{PROJECT_ID}/locations/{LOCATION}/models/{MODEL_ID}
    model="gemini-2.5-flash",
    # Source link: https://storage.cloud.google.com/cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl
    src="gs://cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl",
    config=CreateBatchJobConfig(dest=output_uri),
)
print(f"Job name: {job.name}")
print(f"Job state: {job.state}")
# Example response:
# Job name: projects/%PROJECT_ID%/locations/us-central1/batchPredictionJobs/9876453210000000000
# Job state: JOB_STATE_PENDING

# See the documentation: https://googleapis.github.io/python-genai/genai.html#genai.types.BatchJob
completed_states = {
    JobState.JOB_STATE_SUCCEEDED,
    JobState.JOB_STATE_FAILED,
    JobState.JOB_STATE_CANCELLED,
    JobState.JOB_STATE_PAUSED,
}

while job.state not in completed_states:
    time.sleep(30)
    job = client.batches.get(name=job.name)
    print(f"Job state: {job.state}")
# Example response:
# Job state: JOB_STATE_PENDING
# Job state: JOB_STATE_RUNNING
# Job state: JOB_STATE_RUNNING
# ...
# Job state: JOB_STATE_SUCCEEDED

BigQuery 輸入

import time

from google import genai
from google.genai.types import CreateBatchJobConfig, JobState, HttpOptions

client = genai.Client(http_options=HttpOptions(api_version="v1"))

# TODO(developer): Update and un-comment below line
# output_uri = f"bq://your-project.your_dataset.your_table"

job = client.batches.create(
    # To use a tuned model, set the model param to your tuned model using the following format:
    # model="projects/{PROJECT_ID}/locations/{LOCATION}/models/{MODEL_ID}
    model="gemini-2.5-flash",
    src="bq://storage-samples.generative_ai.batch_requests_for_multimodal_input",
    config=CreateBatchJobConfig(dest=output_uri),
)
print(f"Job name: {job.name}")
print(f"Job state: {job.state}")
# Example response:
# Job name: projects/%PROJECT_ID%/locations/us-central1/batchPredictionJobs/9876453210000000000
# Job state: JOB_STATE_PENDING

# See the documentation: https://googleapis.github.io/python-genai/genai.html#genai.types.BatchJob
completed_states = {
    JobState.JOB_STATE_SUCCEEDED,
    JobState.JOB_STATE_FAILED,
    JobState.JOB_STATE_CANCELLED,
    JobState.JOB_STATE_PAUSED,
}

while job.state not in completed_states:
    time.sleep(30)
    job = client.batches.get(name=job.name)
    print(f"Job state: {job.state}")
# Example response:
# Job state: JOB_STATE_PENDING
# Job state: JOB_STATE_RUNNING
# Job state: JOB_STATE_RUNNING
# ...
# Job state: JOB_STATE_SUCCEEDED

Node.js

在試用這個範例之前，請先按照Node.js使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。詳情請參閱 Vertex AI Node.js API 參考說明文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。

Cloud Storage 輸入

// Import the aiplatform library
const aiplatformLib = require('@google-cloud/aiplatform');
const aiplatform = aiplatformLib.protos.google.cloud.aiplatform.v1;

/**
 * TODO(developer):  Uncomment/update these variables before running the sample.
 */
// projectId = 'YOUR_PROJECT_ID';
// URI of the output folder in Google Cloud Storage.
// E.g. "gs://[BUCKET]/[OUTPUT]"
// outputUri = 'gs://my-bucket';

// URI of the input file in Google Cloud Storage.
// E.g. "gs://[BUCKET]/[DATASET].jsonl"
// Or try:
// "gs://cloud-samples-data/generative-ai/batch/gemini_multimodal_batch_predict.jsonl"
// for a batch prediction that uses audio, video, and an image.
const inputUri =
  'gs://cloud-samples-data/generative-ai/batch/batch_requests_for_multimodal_input.jsonl';
const location = 'us-central1';
const parent = `projects/${projectId}/locations/${location}`;
const modelName = `${parent}/publishers/google/models/gemini-2.0-flash-001`;

// Specify the location of the api endpoint.
const clientOptions = {
  apiEndpoint: `${location}-aiplatform.googleapis.com`,
};

// Instantiate the client.
const jobServiceClient = new aiplatformLib.JobServiceClient(clientOptions);

// Create a Gemini batch prediction job using Google Cloud Storage input and output buckets.
async function create_batch_prediction_gemini_gcs() {
  const gcsSource = new aiplatform.GcsSource({
    uris: [inputUri],
  });

  const inputConfig = new aiplatform.BatchPredictionJob.InputConfig({
    gcsSource: gcsSource,
    instancesFormat: 'jsonl',
  });

  const gcsDestination = new aiplatform.GcsDestination({
    outputUriPrefix: outputUri,
  });

  const outputConfig = new aiplatform.BatchPredictionJob.OutputConfig({
    gcsDestination: gcsDestination,
    predictionsFormat: 'jsonl',
  });

  const batchPredictionJob = new aiplatform.BatchPredictionJob({
    displayName: 'Batch predict with Gemini - GCS',
    model: modelName,
    inputConfig: inputConfig,
    outputConfig: outputConfig,
  });

  const request = {
    parent: parent,
    batchPredictionJob,
  };

  // Create batch prediction job request
  const [response] = await jobServiceClient.createBatchPredictionJob(request);
  console.log('Response name: ', response.name);
  // Example response:
  // Response name: projects/<project>/locations/us-central1/batchPredictionJobs/<job-id>
}

await create_batch_prediction_gemini_gcs();

BigQuery 輸入

// Import the aiplatform library
const aiplatformLib = require('@google-cloud/aiplatform');
const aiplatform = aiplatformLib.protos.google.cloud.aiplatform.v1;

/**
 * TODO(developer):  Uncomment/update these variables before running the sample.
 */
// projectId = 'YOUR_PROJECT_ID';
// URI of the output BigQuery table.
// E.g. "bq://[PROJECT].[DATASET].[TABLE]"
// outputUri = 'bq://projectid.dataset.table';

// URI of the multimodal input BigQuery table.
// E.g. "bq://[PROJECT].[DATASET].[TABLE]"
const inputUri =
  'bq://storage-samples.generative_ai.batch_requests_for_multimodal_input';
const location = 'us-central1';
const parent = `projects/${projectId}/locations/${location}`;
const modelName = `${parent}/publishers/google/models/gemini-2.0-flash-001`;

// Specify the location of the api endpoint.
const clientOptions = {
  apiEndpoint: `${location}-aiplatform.googleapis.com`,
};

// Instantiate the client.
const jobServiceClient = new aiplatformLib.JobServiceClient(clientOptions);

// Create a Gemini batch prediction job using BigQuery input and output datasets.
async function create_batch_prediction_gemini_bq() {
  const bqSource = new aiplatform.BigQuerySource({
    inputUri: inputUri,
  });

  const inputConfig = new aiplatform.BatchPredictionJob.InputConfig({
    bigquerySource: bqSource,
    instancesFormat: 'bigquery',
  });

  const bqDestination = new aiplatform.BigQueryDestination({
    outputUri: outputUri,
  });

  const outputConfig = new aiplatform.BatchPredictionJob.OutputConfig({
    bigqueryDestination: bqDestination,
    predictionsFormat: 'bigquery',
  });

  const batchPredictionJob = new aiplatform.BatchPredictionJob({
    displayName: 'Batch predict with Gemini - BigQuery',
    model: modelName, // Add model parameters per request in the input BigQuery table.
    inputConfig: inputConfig,
    outputConfig: outputConfig,
  });

  const request = {
    parent: parent,
    batchPredictionJob,
  };

  // Create batch prediction job request
  const [response] = await jobServiceClient.createBatchPredictionJob(request);
  console.log('Response name: ', response.name);
  // Example response:
  // Response name: projects/<project>/locations/us-central1/batchPredictionJobs/<job-id>
}

await create_batch_prediction_gemini_bq();

Java

在試用這個範例之前，請先按照Java使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。詳情請參閱 Vertex AI Java API 參考說明文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。

Cloud Storage 輸入

import com.google.cloud.aiplatform.v1.BatchPredictionJob;
import com.google.cloud.aiplatform.v1.GcsDestination;
import com.google.cloud.aiplatform.v1.GcsSource;
import com.google.cloud.aiplatform.v1.JobServiceClient;
import com.google.cloud.aiplatform.v1.JobServiceSettings;
import com.google.cloud.aiplatform.v1.LocationName;
import java.io.IOException;

public class CreateBatchPredictionGeminiJobSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Update these variables before running the sample.
    String project = "PROJECT_ID";
    String gcsDestinationOutputUriPrefix = "gs://MY_BUCKET/";

    createBatchPredictionGeminiJobSample(project, gcsDestinationOutputUriPrefix);
  }

  // Create a batch prediction job using a JSONL input file and output URI, both in Cloud
  // Storage.
  public static BatchPredictionJob createBatchPredictionGeminiJobSample(
      String project, String gcsDestinationOutputUriPrefix) throws IOException {
    String location = "us-central1";
    JobServiceSettings settings =
        JobServiceSettings.newBuilder()
            .setEndpoint(String.format("%s-aiplatform.googleapis.com:443", location))
            .build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests.
    try (JobServiceClient client = JobServiceClient.create(settings)) {
      GcsSource gcsSource =
          GcsSource.newBuilder()
              .addUris(
                  "gs://cloud-samples-data/generative-ai/batch/"
                      + "batch_requests_for_multimodal_input.jsonl")
              // Or try
              // "gs://cloud-samples-data/generative-ai/batch/gemini_multimodal_batch_predict.jsonl"
              // for a batch prediction that uses audio, video, and an image.
              .build();
      BatchPredictionJob.InputConfig inputConfig =
          BatchPredictionJob.InputConfig.newBuilder()
              .setInstancesFormat("jsonl")
              .setGcsSource(gcsSource)
              .build();
      GcsDestination gcsDestination =
          GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();
      BatchPredictionJob.OutputConfig outputConfig =
          BatchPredictionJob.OutputConfig.newBuilder()
              .setPredictionsFormat("jsonl")
              .setGcsDestination(gcsDestination)
              .build();
      String modelName =
          String.format(
              "projects/%s/locations/%s/publishers/google/models/%s",
              project, location, "gemini-2.0-flash-001");

      BatchPredictionJob batchPredictionJob =
          BatchPredictionJob.newBuilder()
              .setDisplayName("my-display-name")
              .setModel(modelName) // Add model parameters per request in the input jsonl file.
              .setInputConfig(inputConfig)
              .setOutputConfig(outputConfig)
              .build();

      LocationName parent = LocationName.of(project, location);
      BatchPredictionJob response = client.createBatchPredictionJob(parent, batchPredictionJob);
      System.out.format("\tName: %s\n", response.getName());
      // Example response:
      //   Name: projects/<project>/locations/us-central1/batchPredictionJobs/<job-id>
      return response;
    }
  }
}

BigQuery 輸入

import com.google.cloud.aiplatform.v1.BatchPredictionJob;
import com.google.cloud.aiplatform.v1.BigQueryDestination;
import com.google.cloud.aiplatform.v1.BigQuerySource;
import com.google.cloud.aiplatform.v1.JobServiceClient;
import com.google.cloud.aiplatform.v1.JobServiceSettings;
import com.google.cloud.aiplatform.v1.LocationName;
import java.io.IOException;

public class CreateBatchPredictionGeminiBigqueryJobSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Update these variables before running the sample.
    String project = "PROJECT_ID";
    String bigqueryDestinationOutputUri = "bq://PROJECT_ID.MY_DATASET.MY_TABLE";

    createBatchPredictionGeminiBigqueryJobSample(project, bigqueryDestinationOutputUri);
  }

  // Create a batch prediction job using BigQuery input and output datasets.
  public static BatchPredictionJob createBatchPredictionGeminiBigqueryJobSample(
      String project, String bigqueryDestinationOutputUri) throws IOException {
    String location = "us-central1";
    JobServiceSettings settings =
        JobServiceSettings.newBuilder()
            .setEndpoint(String.format("%s-aiplatform.googleapis.com:443", location))
            .build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests.
    try (JobServiceClient client = JobServiceClient.create(settings)) {
      BigQuerySource bigquerySource =
          BigQuerySource.newBuilder()
              .setInputUri("bq://storage-samples.generative_ai.batch_requests_for_multimodal_input")
              .build();
      BatchPredictionJob.InputConfig inputConfig =
          BatchPredictionJob.InputConfig.newBuilder()
              .setInstancesFormat("bigquery")
              .setBigquerySource(bigquerySource)
              .build();
      BigQueryDestination bigqueryDestination =
          BigQueryDestination.newBuilder().setOutputUri(bigqueryDestinationOutputUri).build();
      BatchPredictionJob.OutputConfig outputConfig =
          BatchPredictionJob.OutputConfig.newBuilder()
              .setPredictionsFormat("bigquery")
              .setBigqueryDestination(bigqueryDestination)
              .build();
      String modelName =
          String.format(
              "projects/%s/locations/%s/publishers/google/models/%s",
              project, location, "gemini-2.0-flash-001");

      BatchPredictionJob batchPredictionJob =
          BatchPredictionJob.newBuilder()
              .setDisplayName("my-display-name")
              .setModel(modelName) // Add model parameters per request in the input BigQuery table.
              .setInputConfig(inputConfig)
              .setOutputConfig(outputConfig)
              .build();

      LocationName parent = LocationName.of(project, location);
      BatchPredictionJob response = client.createBatchPredictionJob(parent, batchPredictionJob);
      System.out.format("\tName: %s\n", response.getName());
      // Example response:
      //   Name: projects/<project>/locations/us-central1/batchPredictionJobs/<job-id>
      return response;
    }
  }
}

Go

在試用這個範例之前，請先按照Go使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。詳情請參閱 Vertex AI Go API 參考說明文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。

Cloud Storage 輸入

import (
	"context"
	"fmt"
	"io"
	"time"

	aiplatform "cloud.google.com/go/aiplatform/apiv1"
	aiplatformpb "cloud.google.com/go/aiplatform/apiv1/aiplatformpb"

	"google.golang.org/api/option"
	"google.golang.org/protobuf/types/known/structpb"
)

// batchPredictGCS submits a batch prediction job using GCS data source as its input
func batchPredictGCS(w io.Writer, projectID, location string, inputURIs []string, outputURI string) error {
	// location := "us-central1"
	// inputURIs := []string{"gs://cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl"}
	// outputURI := "gs://<cloud-bucket-name>/<prefix-name>"
	modelName := "gemini-2.0-flash-001"
	jobName := "batch-predict-gcs-test-001"

	ctx := context.Background()
	apiEndpoint := fmt.Sprintf("%s-aiplatform.googleapis.com:443", location)
	client, err := aiplatform.NewJobClient(ctx, option.WithEndpoint(apiEndpoint))
	if err != nil {
		return fmt.Errorf("unable to create aiplatform client: %w", err)
	}
	defer client.Close()

	modelParameters, err := structpb.NewValue(map[string]interface{}{
		"temperature":     0.2,
		"maxOutputTokens": 200,
	})
	if err != nil {
		return fmt.Errorf("unable to convert model parameters to protobuf value: %w", err)
	}

	req := &aiplatformpb.CreateBatchPredictionJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		BatchPredictionJob: &aiplatformpb.BatchPredictionJob{
			DisplayName:     jobName,
			Model:           fmt.Sprintf("publishers/google/models/%s", modelName),
			ModelParameters: modelParameters,
			// Check the API reference for `BatchPredictionJob` for supported input and output formats:
			// https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#google.cloud.aiplatform.v1.BatchPredictionJob
			InputConfig: &aiplatformpb.BatchPredictionJob_InputConfig{
				Source: &aiplatformpb.BatchPredictionJob_InputConfig_GcsSource{
					GcsSource: &aiplatformpb.GcsSource{
						Uris: inputURIs,
					},
				},
				InstancesFormat: "jsonl",
			},
			OutputConfig: &aiplatformpb.BatchPredictionJob_OutputConfig{
				Destination: &aiplatformpb.BatchPredictionJob_OutputConfig_GcsDestination{
					GcsDestination: &aiplatformpb.GcsDestination{
						OutputUriPrefix: outputURI,
					},
				},
				PredictionsFormat: "jsonl",
			},
		},
	}

	job, err := client.CreateBatchPredictionJob(ctx, req)
	if err != nil {
		return err
	}
	fullJobId := job.GetName()
	fmt.Fprintf(w, "submitted batch predict job for model %q\n", job.GetModel())
	fmt.Fprintf(w, "job id: %q\n", fullJobId)
	fmt.Fprintf(w, "job state: %s\n", job.GetState())
	// Example response:
	// submitted batch predict job for model "publishers/google/models/gemini-2.0-flash-001"
	// job id: "projects/.../locations/.../batchPredictionJobs/1234567890000000000"
	// job state: JOB_STATE_PENDING

	for {
		time.Sleep(5 * time.Second)

		job, err := client.GetBatchPredictionJob(ctx, &aiplatformpb.GetBatchPredictionJobRequest{
			Name: fullJobId,
		})
		if err != nil {
			return fmt.Errorf("error: couldn't get updated job state: %w", err)
		}

		if job.GetEndTime() != nil {
			fmt.Fprintf(w, "batch predict job finished with state %s\n", job.GetState())
			break
		} else {
			fmt.Fprintf(w, "batch predict job is running... job state is %s\n", job.GetState())
		}
	}

	return nil
}

BigQuery 輸入

import (
	"context"
	"fmt"
	"io"
	"time"

	aiplatform "cloud.google.com/go/aiplatform/apiv1"
	aiplatformpb "cloud.google.com/go/aiplatform/apiv1/aiplatformpb"

	"google.golang.org/api/option"
	"google.golang.org/protobuf/types/known/structpb"
)

// batchPredictBQ submits a batch prediction job using BigQuery data source as its input
func batchPredictBQ(w io.Writer, projectID, location string, inputURI string, outputURI string) error {
	// location  := "us-central1"
	// inputURI  := "bq://storage-samples.generative_ai.batch_requests_for_multimodal_input"
	// outputURI := "bq://<cloud-project-name>.<dataset-name>.<table-name>"
	modelName := "gemini-2.0-flash-001"
	jobName := "batch-predict-bq-test-001"

	ctx := context.Background()
	apiEndpoint := fmt.Sprintf("%s-aiplatform.googleapis.com:443", location)
	client, err := aiplatform.NewJobClient(ctx, option.WithEndpoint(apiEndpoint))
	if err != nil {
		return fmt.Errorf("unable to create aiplatform client: %w", err)
	}
	defer client.Close()

	modelParameters, err := structpb.NewValue(map[string]interface{}{
		"temperature":     0.2,
		"maxOutputTokens": 200,
	})
	if err != nil {
		return fmt.Errorf("unable to convert model parameters to protobuf value: %w", err)
	}

	req := &aiplatformpb.CreateBatchPredictionJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		BatchPredictionJob: &aiplatformpb.BatchPredictionJob{
			DisplayName:     jobName,
			Model:           fmt.Sprintf("publishers/google/models/%s", modelName),
			ModelParameters: modelParameters,
			// Check the API reference for `BatchPredictionJob` for supported input and output formats:
			// https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#google.cloud.aiplatform.v1.BatchPredictionJob
			InputConfig: &aiplatformpb.BatchPredictionJob_InputConfig{
				Source: &aiplatformpb.BatchPredictionJob_InputConfig_BigquerySource{
					BigquerySource: &aiplatformpb.BigQuerySource{
						InputUri: inputURI,
					},
				},
				InstancesFormat: "bigquery",
			},

			OutputConfig: &aiplatformpb.BatchPredictionJob_OutputConfig{
				Destination: &aiplatformpb.BatchPredictionJob_OutputConfig_BigqueryDestination{
					BigqueryDestination: &aiplatformpb.BigQueryDestination{
						OutputUri: outputURI,
					},
				},
				PredictionsFormat: "bigquery",
			},
		},
	}

	job, err := client.CreateBatchPredictionJob(ctx, req)
	if err != nil {
		return err
	}
	fullJobId := job.GetName()
	fmt.Fprintf(w, "submitted batch predict job for model %q\n", job.GetModel())
	fmt.Fprintf(w, "job id: %q\n", fullJobId)
	fmt.Fprintf(w, "job state: %s\n", job.GetState())
	// Example response:
	// submitted batch predict job for model "publishers/google/models/gemini-2.0-flash-001"
	// job id: "projects/.../locations/.../batchPredictionJobs/1234567890000000000"
	// job state: JOB_STATE_PENDING

	for {
		time.Sleep(5 * time.Second)

		job, err := client.GetBatchPredictionJob(ctx, &aiplatformpb.GetBatchPredictionJobRequest{
			Name: fullJobId,
		})
		if err != nil {
			return fmt.Errorf("error: couldn't get updated job state: %w", err)
		}

		if job.GetEndTime() != nil {
			fmt.Fprintf(w, "batch predict job finished with state %s\n", job.GetState())
			break
		} else {
			fmt.Fprintf(w, "batch predict job is running... job state is %s\n", job.GetState())
		}
	}

	return nil
}

擷取批次輸出內容

批次預測工作完成後，系統會根據您的要求，將輸出內容儲存在指定的 Cloud Storage 值區或 BigQuery 表格。

後續步驟

請參閱「 Gemini 模型微調總覽」，瞭解如何微調 Gemini 模型。
進一步瞭解如何取得 Gemini 的批次預測結果。

取得 Gemini 的批次預測結果 透過集合功能整理內容 你可以依據偏好儲存及分類內容。

支援的模型

語法範例

參數

要求主體

inputConfig

outputConfig

範例

要求批次回應

REST

Cloud Storage 輸入

curl

PowerShell

回應

BigQuery 輸入

curl

PowerShell

回應

Python

安裝

Cloud Storage 輸入

BigQuery 輸入

Node.js

Cloud Storage 輸入

BigQuery 輸入

Java

Cloud Storage 輸入

BigQuery 輸入

Go

Cloud Storage 輸入

BigQuery 輸入

擷取批次輸出內容

後續步驟

取得 Gemini 的批次預測結果

`inputConfig`

`outputConfig`