Create a batch prediction job for text entity extraction

Creates a batch prediction job for text entity extraction using the create_batch_prediction_job method.

Explore further

For detailed documentation that includes this code sample, see the following:

• Get predictions from a text entity extraction model

Code sample

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.api.gax.rpc.ApiException;
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 com.google.cloud.aiplatform.v1.ModelName;
import java.io.IOException;

public class CreateBatchPredictionJobTextEntityExtractionSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "PROJECT";
    String location = "us-central1";
    String displayName = "DISPLAY_NAME";
    String modelId = "MODEL_ID";
    String gcsSourceUri = "GCS_SOURCE_URI";
    String gcsDestinationOutputUriPrefix = "GCS_DESTINATION_OUTPUT_URI_PREFIX";
    createBatchPredictionJobTextEntityExtractionSample(
        project, location, displayName, modelId, gcsSourceUri, gcsDestinationOutputUriPrefix);
  }

  static void createBatchPredictionJobTextEntityExtractionSample(
      String project,
      String location,
      String displayName,
      String modelId,
      String gcsSourceUri,
      String gcsDestinationOutputUriPrefix)
      throws IOException {
    // The AI Platform services require regional API endpoints.
    JobServiceSettings settings =
        JobServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .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. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (JobServiceClient client = JobServiceClient.create(settings)) {
      try {
        String modelName = ModelName.of(project, location, modelId).toString();
        GcsSource gcsSource = GcsSource.newBuilder().addUris(gcsSourceUri).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();
        BatchPredictionJob batchPredictionJob =
            BatchPredictionJob.newBuilder()
                .setDisplayName(displayName)
                .setModel(modelName)
                .setInputConfig(inputConfig)
                .setOutputConfig(outputConfig)
                .build();
        LocationName parent = LocationName.of(project, location);
        BatchPredictionJob response = client.createBatchPredictionJob(parent, batchPredictionJob);
        System.out.format("response: %s\n", response);
        System.out.format("\tname:%s\n", response.getName());
      } catch (ApiException ex) {
        System.out.format("Exception: %s\n", ex.getLocalizedMessage());
      }
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Node.js API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const batchPredictionDisplayName = 'YOUR_BATCH_PREDICTION_DISPLAY_NAME';
// const modelId = 'YOUR_MODEL_ID';
// const gcsSourceUri = 'YOUR_GCS_SOURCE_URI';
// const gcsDestinationOutputUriPrefix = 'YOUR_GCS_DEST_OUTPUT_URI_PREFIX';
//    eg. "gs://<your-gcs-bucket>/destination_path"
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

// Imports the Google Cloud Job Service Client library
const {JobServiceClient} = require('@google-cloud/aiplatform').v1;

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const jobServiceClient = new JobServiceClient(clientOptions);

async function createBatchPredictionJobTextEntityExtraction() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}`;
  const modelName = `projects/${project}/locations/${location}/models/${modelId}`;

  const inputConfig = {
    instancesFormat: 'jsonl',
    gcsSource: {uris: [gcsSourceUri]},
  };
  const outputConfig = {
    predictionsFormat: 'jsonl',
    gcsDestination: {outputUriPrefix: gcsDestinationOutputUriPrefix},
  };
  const batchPredictionJob = {
    displayName: batchPredictionDisplayName,
    model: modelName,
    inputConfig,
    outputConfig,
  };
  const request = {
    parent,
    batchPredictionJob,
  };

  // Create batch prediction job request
  const [response] = await jobServiceClient.createBatchPredictionJob(request);

  console.log('Create batch prediction job text entity extraction response');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
createBatchPredictionJobTextEntityExtraction();

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value


def create_batch_prediction_job_text_entity_extraction_sample(
    project: str,
    display_name: str,
    model_name: str,
    gcs_source_uri: str,
    gcs_destination_output_uri_prefix: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.JobServiceClient(client_options=client_options)
    model_parameters_dict = {}
    model_parameters = json_format.ParseDict(model_parameters_dict, Value())

    batch_prediction_job = {
        "display_name": display_name,
        # Format: 'projects/{project}/locations/{location}/models/{model_id}'
        "model": model_name,
        "model_parameters": model_parameters,
        "input_config": {
            "instances_format": "jsonl",
            "gcs_source": {"uris": [gcs_source_uri]},
        },
        "output_config": {
            "predictions_format": "jsonl",
            "gcs_destination": {"output_uri_prefix": gcs_destination_output_uri_prefix},
        },
    }
    parent = f"projects/{project}/locations/{location}"
    response = client.create_batch_prediction_job(
        parent=parent, batch_prediction_job=batch_prediction_job
    )
    print("response:", response)

What's next

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