Batch prediction for BigQuery

This page describes how to get batch predictions using BigQuery.

1. Prepare your inputs

BigQuery storage input

    gcloud projects add-iam-policy-binding PROJECT_ID \
        --member="serviceAccount:SERVICE_ACCOUNT_ID@PROJECT_ID.iam.gserviceaccount.com" \
        --role="roles/bigquery.user"

Replace the following values:

*   <var>PROJECT_ID</var>: The project that your service account was
    created in.
*   <var>SERVICE_ACCOUNT_ID</var>: The ID for the service account.
  • A request column is required, and must be valid JSON. This JSON data represents your input for the model.
  • The content in the request column must match the structure of a GenerateContentRequest. + Your input table can have column data types other than request. These columns can have BigQuery data types except for the following: array, struct, range, datetime, and geography. These columns are ignored for content generation but included in the output table.
Example input (JSON) 
        
{
  "contents": [
    {
      "role": "user",
      "parts": [
        {
          "text": "Give me a recipe for banana bread."
        }
      ]
    }
  ],
  "system_instruction": {
    "parts": [
      {
        "text": "You are a chef."
      }
    ]
  }
}

2. Submit a batch job

You can create a batch job through the Google Cloud console, the Google Gen AI SDK, or the REST API.

The job and your table must be in the same region.

Console

  1. In the Vertex AI section of the Google Cloud console, go to the Batch Inference page.

    Go to Batch Inference

  2. Click Create.

REST

To create a batch prediction job, use the projects.locations.batchPredictionJobs.create method.

Before using any of the request data, make the following replacements:

  • LOCATION: A region that supports Gemini models.
  • PROJECT_ID: Your project ID.
  • MODEL_PATH: the publisher model name, for example, publishers/google/models/gemini-2.0-flash-001; or the tuned endpoint name, for example, projects/PROJECT_ID/locations/LOCATION/models/MODEL_ID, where MODEL_ID is the model ID of the tuned model.
  • INPUT_URI: The BigQuery table where your batch prediction input is located such as bq://myproject.mydataset.input_table. The dataset must be located in the same region as the batch prediction job. Multi-region datasets are not supported.
  • OUTPUT_FORMAT: To output to a BigQuery table, specify bigquery. To output to a Cloud Storage bucket, specify jsonl.
  • DESTINATION: For BigQuery, specify bigqueryDestination. For Cloud Storage, specify gcsDestination.
  • OUTPUT_URI_FIELD_NAME: For BigQuery, specify outputUri. For Cloud Storage, specify outputUriPrefix.
  • OUTPUT_URI: For BigQuery, specify the table location such as bq://myproject.mydataset.output_result. The region of the output BigQuery dataset must be the same as the Vertex AI batch prediction job. For Cloud Storage, specify the bucket and directory location such as gs://mybucket/path/to/output.

HTTP method and URL: 

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs

Request JSON body: 

{
  "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"
    }
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

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

Save the request body in a file named request.json, and execute the following command: 

$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

You should receive a JSON response similar to the following.

The response includes a unique identifier for the batch job. You can poll for the status of the batch job using the BATCH_JOB_ID. For more information, see Monitor the job status. Note: Custom Service account, live progress, CMEK, and VPCSC reports are not supported.

Python

Install

pip install --upgrade google-genai

To learn more, see the SDK reference documentation.

Set environment variables to use the Gen AI SDK with Vertex AI: 

# 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
 

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

3. Monitor the job status and progress

After the job is submitted, you can check the status of your batch job using API, SDK and Cloud Console.

Console

  1. Go to the Batch Inference page.

    Go to Batch Inference

  2. Select your batch job to monitor its progress.

REST

To monitor a batch prediction job, use the projects.locations.batchPredictionJobs.get method and view the CompletionStats field in the response.

Before using any of the request data, make the following replacements:

  • LOCATION: A region that supports Gemini models.
  • PROJECT_ID: .
  • BATCH_JOB_ID: Your batch job ID.

HTTP method and URL: 

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs/BATCH_JOB_ID

To send your request, choose one of these options:

curl

Execute the following command: 

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs/BATCH_JOB_ID"

PowerShell

Execute the following command: 

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

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs/BATCH_JOB_ID" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Python

Install

pip install --upgrade google-genai

To learn more, see the SDK reference documentation.

Set environment variables to use the Gen AI SDK with Vertex AI: 

# 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
 

from google import genai
from google.genai.types import HttpOptions

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

# Get the batch job
# Eg. batch_job_name = "projects/123456789012/locations/us-central1/batchPredictionJobs/1234567890123456789"
batch_job = client.batches.get(name=batch_job_name)

print(f"Job state: {batch_job.state}")
# Example response:
# Job state: JOB_STATE_PENDING
# Job state: JOB_STATE_RUNNING
# Job state: JOB_STATE_SUCCEEDED

The status of the a given batch job can be any of the following:

  • JOB_STATE_PENDING: Queue for capacity. The job can be in queue state up to 72-hour before entering running state.
  • JOB_STATE_RUNNING: The input file was successfully validated and the batch is currently being run.
  • JOB_STATE_SUCCEEDED: The batch has been completed and the results are ready
  • JOB_STATE_FAILED: the input file has failed the validation process, or could not be completed within the 24-hour time window after entering RUNNING state.
  • JOB_STATE_CANCELLING: the batch is being cancelled
  • JOB_STATE_CANCELLED: the batch was cancelled

4. Retrieve batch output

When a batch prediction task completes, the output is stored in the BigQuery table that you specified in your request.

For succeeded rows, model responses are stored in the response column. Otherwise, error details are stored in the status column for further inspection.

Output example

Successful example

{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [
          {
            "text": "In a medium bowl, whisk together the flour, baking soda, baking powder."
          }
        ]
      },
      "finishReason": "STOP",
      "safetyRatings": [
        {
          "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.14057204,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.14270912
        }
      ]
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 8,
    "candidatesTokenCount": 396,
    "totalTokenCount": 404
  }
}

Failed example

  • Request

    {"contents":[{"parts":{"text":"Explain how AI works in a few words."},"role":"tester"}]}

  • Response

    Bad Request: {"error": {"code": 400, "message": "Please use a valid role: user, model.", "status": "INVALID_ARGUMENT"}}