Vorhersage für die Extraktion von Textentitäten

Ruft eine Vorhersage für die Extraktion der Textentität mit der Vorhersagemethode ab.

Weitere Informationen

Eine ausführliche Dokumentation, die dieses Codebeispiel enthält, finden Sie hier:

Codebeispiel

Java

Bevor Sie dieses Beispiel anwenden, folgen Sie den Java-Einrichtungsschritten in der Vertex AI-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur Vertex AI Java API.

Richten Sie zur Authentifizierung bei Vertex AI Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten. 


import com.google.cloud.aiplatform.util.ValueConverter;
import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.PredictResponse;
import com.google.cloud.aiplatform.v1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1.PredictionServiceSettings;
import com.google.cloud.aiplatform.v1.schema.predict.instance.TextExtractionPredictionInstance;
import com.google.cloud.aiplatform.v1.schema.predict.prediction.TextExtractionPredictionResult;
import com.google.protobuf.Value;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

public class PredictTextEntityExtractionSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String content = "YOUR_TEXT_CONTENT";
    String endpointId = "YOUR_ENDPOINT_ID";

    predictTextEntityExtraction(project, content, endpointId);
  }

  static void predictTextEntityExtraction(String project, String content, String endpointId)
      throws IOException {
    PredictionServiceSettings predictionServiceSettings =
        PredictionServiceSettings.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 (PredictionServiceClient predictionServiceClient =
        PredictionServiceClient.create(predictionServiceSettings)) {
      String location = "us-central1";
      String jsonString = "{\"content\": \"" + content + "\"}";

      EndpointName endpointName = EndpointName.of(project, location, endpointId);

      TextExtractionPredictionInstance instance =
          TextExtractionPredictionInstance.newBuilder().setContent(content).build();

      List<Value> instances = new ArrayList<>();
      instances.add(ValueConverter.toValue(instance));

      PredictResponse predictResponse =
          predictionServiceClient.predict(endpointName, instances, ValueConverter.EMPTY_VALUE);
      System.out.println("Predict Text Entity Extraction Response");
      System.out.format("\tDeployed Model Id: %s\n", predictResponse.getDeployedModelId());

      System.out.println("Predictions");
      for (Value prediction : predictResponse.getPredictionsList()) {
        TextExtractionPredictionResult.Builder resultBuilder =
            TextExtractionPredictionResult.newBuilder();

        TextExtractionPredictionResult result =
            (TextExtractionPredictionResult) ValueConverter.fromValue(resultBuilder, prediction);

        for (int i = 0; i < result.getIdsCount(); i++) {
          long textStartOffset = result.getTextSegmentStartOffsets(i);
          long textEndOffset = result.getTextSegmentEndOffsets(i);
          String entity = content.substring((int) textStartOffset, (int) textEndOffset);

          System.out.format("\tEntity: %s\n", entity);
          System.out.format("\tEntity type: %s\n", result.getDisplayNames(i));
          System.out.format("\tConfidences: %f\n", result.getConfidences(i));
          System.out.format("\tIDs: %d\n", result.getIds(i));
        }
      }
    }
  }
}

Node.js

Bevor Sie dieses Beispiel anwenden, folgen Sie den Node.js-Einrichtungsschritten in der Vertex AI-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur Vertex AI Node.js API.

Richten Sie zur Authentifizierung bei Vertex AI Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten. 

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

// const text = "YOUR_PREDICTION_TEXT";
// const endpointId = "YOUR_ENDPOINT_ID";
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const aiplatform = require('@google-cloud/aiplatform');
const {instance, prediction} =
  aiplatform.protos.google.cloud.aiplatform.v1.schema.predict;

// Imports the Google Cloud Model Service Client library
const {PredictionServiceClient} = aiplatform.v1;

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

// Instantiates a client
const predictionServiceClient = new PredictionServiceClient(clientOptions);

async function predictTextEntityExtraction() {
  // Configure the endpoint resource
  const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;

  const instanceObj = new instance.TextExtractionPredictionInstance({
    content: text,
  });
  const instanceVal = instanceObj.toValue();
  const instances = [instanceVal];

  const request = {
    endpoint,
    instances,
  };

  // Predict request
  const [response] = await predictionServiceClient.predict(request);

  console.log('Predict text entity extraction response :');
  console.log(`\tDeployed model id : ${response.deployedModelId}`);

  console.log('\nPredictions :');
  for (const predictionResultValue of response.predictions) {
    const predictionResult =
      prediction.TextExtractionPredictionResult.fromValue(
        predictionResultValue
      );

    for (const [i, label] of predictionResult.displayNames.entries()) {
      const textStartOffset = parseInt(
        predictionResult.textSegmentStartOffsets[i]
      );
      const textEndOffset = parseInt(
        predictionResult.textSegmentEndOffsets[i]
      );
      const entity = text.substring(textStartOffset, textEndOffset);
      console.log(`\tEntity: ${entity}`);
      console.log(`\tEntity type: ${label}`);
      console.log(`\tConfidences: ${predictionResult.confidences[i]}`);
      console.log(`\tIDs: ${predictionResult.ids[i]}\n\n`);
    }
  }
}
predictTextEntityExtraction();

Python

Bevor Sie dieses Beispiel anwenden, folgen Sie den Python-Einrichtungsschritten in der Vertex AI-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur Vertex AI Python API.

Richten Sie zur Authentifizierung bei Vertex AI Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten. 

from google.cloud import aiplatform
from google.cloud.aiplatform.gapic.schema import predict
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value


def predict_text_entity_extraction_sample(
    project: str,
    endpoint_id: str,
    content: 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.PredictionServiceClient(client_options=client_options)
    # The format of each instance should conform to the deployed model's prediction input schema
    instance = predict.instance.TextExtractionPredictionInstance(
        content=content,
    ).to_value()
    instances = [instance]
    parameters_dict = {}
    parameters = json_format.ParseDict(parameters_dict, Value())
    endpoint = client.endpoint_path(
        project=project, location=location, endpoint=endpoint_id
    )
    response = client.predict(
        endpoint=endpoint, instances=instances, parameters=parameters
    )
    print("response")
    print(" deployed_model_id:", response.deployed_model_id)
    # See gs://google-cloud-aiplatform/schema/predict/prediction/text_extraction_1.0.0.yaml for the format of the predictions.
    predictions = response.predictions
    for prediction in predictions:
        print(" prediction:", dict(prediction))

Nächste Schritte

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