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Batch Predict with Gemini using BigQuery data
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Perform batch text prediction with Gemini using BigQuery data source as input.
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For detailed documentation that includes this code sample, see the following:
Code sample
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[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],[],[],[],null,["# Batch Predict with Gemini using BigQuery data\n\nPerform batch text prediction with Gemini using BigQuery data source as input.\n\nExplore further\n---------------\n\n\nFor detailed documentation that includes this code sample, see the following:\n\n- [Batch prediction for BigQuery](/vertex-ai/generative-ai/docs/multimodal/batch-prediction-from-bigquery)\n- [Get batch predictions for Gemini](/vertex-ai/generative-ai/docs/model-reference/batch-prediction-api)\n\nCode sample\n-----------\n\n### Python\n\n\nBefore trying this sample, follow the Python setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Python API\nreference documentation](/python/docs/reference/aiplatform/latest).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n import time\n\n from google import genai\n from google.genai.types import CreateBatchJobConfig, JobState, HttpOptions\n\n client = genai.Client(http_options=HttpOptions(api_version=\"v1\"))\n\n # TODO(developer): Update and un-comment below line\n # output_uri = f\"bq://your-project.your_dataset.your_table\"\n\n job = client.batches.create(\n # To use a tuned model, set the model param to your tuned model using the following format:\n # model=\"projects/{PROJECT_ID}/locations/{LOCATION}/models/{MODEL_ID}\n model=\"gemini-2.5-flash\",\n src=\"bq://storage-samples.generative_ai.batch_requests_for_multimodal_input\",\n config=CreateBatchJobConfig(dest=output_uri),\n )\n print(f\"Job name: {job.name}\")\n print(f\"Job state: {job.state}\")\n # Example response:\n # Job name: projects/%PROJECT_ID%/locations/us-central1/batchPredictionJobs/9876453210000000000\n # Job state: JOB_STATE_PENDING\n\n # See the documentation: https://googleapis.github.io/python-genai/genai.html#genai.types.BatchJob\n completed_states = {\n JobState.JOB_STATE_SUCCEEDED,\n JobState.JOB_STATE_FAILED,\n JobState.JOB_STATE_CANCELLED,\n JobState.JOB_STATE_PAUSED,\n }\n\n while job.state not in completed_states:\n time.sleep(30)\n job = client.batches.get(name=job.name)\n print(f\"Job state: {job.state}\")\n # Example response:\n # Job state: JOB_STATE_PENDING\n # Job state: JOB_STATE_RUNNING\n # Job state: JOB_STATE_RUNNING\n # ...\n # Job state: JOB_STATE_SUCCEEDED\n\nWhat's next\n-----------\n\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=googlegenaisdk)."]]