Meringkas konten teks menggunakan AI Generatif (AI Generatif)

Meringkas konten teks menggunakan model teks penayang.

Contoh kode

Java

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Java di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Java Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

// Text Summarization with a Large Language Model
public class PredictTextSummarizationSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    // Designing prompts for text summerization with supported large language models:
    // https://cloud.google.com/vertex-ai/docs/generative-ai/text/summarization-prompts
    String instance =
        "{ \"content\": \"Background: There is evidence that there have been significant changes \n"
            + "in Amazon rainforest vegetation over the last 21,000 years through the Last \n"
            + "Glacial Maximum (LGM) and subsequent deglaciation. Analyses of sediment \n"
            + "deposits from Amazon basin paleo lakes and from the Amazon Fan indicate that \n"
            + "rainfall in the basin during the LGM was lower than for the present, and this \n"
            + "was almost certainly associated with reduced moist tropical vegetation cover \n"
            + "in the basin. There is debate, however, over how extensive this reduction \n"
            + "was. Some scientists argue that the rainforest was reduced to small, isolated \n"
            + "refugia separated by open forest and grassland; other scientists argue that \n"
            + "the rainforest remained largely intact but extended less far to the north, \n"
            + "south, and east than is seen today. This debate has proved difficult to \n"
            + "resolve because the practical limitations of working in the rainforest mean \n"
            + "that data sampling is biased away from the center of the Amazon basin, and \n"
            + "both explanations are reasonably well supported by the available data.\n"
            + "\n"
            + "Q: What does LGM stands for?\n"
            + "A: Last Glacial Maximum.\n"
            + "\n"
            + "Q: What did the analysis from the sediment deposits indicate?\n"
            + "A: Rainfall in the basin during the LGM was lower than for the present.\n"
            + "\n"
            + "Q: What are some of scientists arguments?\n"
            + "A: The rainforest was reduced to small, isolated refugia separated by open forest"
            + " and grassland.\n"
            + "\n"
            + "Q: There have been major changes in Amazon rainforest vegetation over the last how"
            + " many years?\n"
            + "A: 21,000.\n"
            + "\n"
            + "Q: What caused changes in the Amazon rainforest vegetation?\n"
            + "A: The Last Glacial Maximum (LGM) and subsequent deglaciation\n"
            + "\n"
            + "Q: What has been analyzed to compare Amazon rainfall in the past and present?\n"
            + "A: Sediment deposits.\n"
            + "\n"
            + "Q: What has the lower rainfall in the Amazon during the LGM been attributed to?\n"
            + "A:\"}";
    String parameters =
        "{\n"
            + "  \"temperature\": 0,\n"
            + "  \"maxOutputTokens\": 32,\n"
            + "  \"topP\": 0,\n"
            + "  \"topK\": 1\n"
            + "}";
    String project = "YOUR_PROJECT_ID";
    String location = "us-central1";
    String publisher = "google";
    String model = "text-bison@001";

    predictTextSummarization(instance, parameters, project, location, publisher, model);
  }

  // Get summarization from a supported text model
  public static void predictTextSummarization(
      String instance,
      String parameters,
      String project,
      String location,
      String publisher,
      String model)
      throws IOException {
    String endpoint = String.format("%s-aiplatform.googleapis.com:443", location);
    PredictionServiceSettings predictionServiceSettings =
        PredictionServiceSettings.newBuilder()
            .setEndpoint(endpoint)
            .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 (PredictionServiceClient predictionServiceClient =
        PredictionServiceClient.create(predictionServiceSettings)) {
      final EndpointName endpointName =
          EndpointName.ofProjectLocationPublisherModelName(project, location, publisher, model);

      // Use Value.Builder to convert instance to a dynamically typed value that can be
      // processed by the service.
      Value.Builder instanceValue = Value.newBuilder();
      JsonFormat.parser().merge(instance, instanceValue);
      List<Value> instances = new ArrayList<>();
      instances.add(instanceValue.build());

      // Use Value.Builder to convert parameter to a dynamically typed value that can be
      // processed by the service.
      Value.Builder parameterValueBuilder = Value.newBuilder();
      JsonFormat.parser().merge(parameters, parameterValueBuilder);
      Value parameterValue = parameterValueBuilder.build();

      PredictResponse predictResponse =
          predictionServiceClient.predict(endpointName, instances, parameterValue);
      System.out.println("Predict Response");
      System.out.println(predictResponse);
    }
  }
}

Langkah berikutnya

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