Menguji perintah teks (Generative AI)

Uji perintah pengujian untuk menghasilkan ide 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;

public class PredictTextPromptSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    // Details of designing text prompts for supported large language models:
    // https://cloud.google.com/vertex-ai/docs/generative-ai/text/text-overview
    String instance =
        "{ \"prompt\": " + "\"Give me ten interview questions for the role of program manager.\"}";
    String parameters =
        "{\n"
            + "  \"temperature\": 0.2,\n"
            + "  \"maxOutputTokens\": 256,\n"
            + "  \"topP\": 0.95,\n"
            + "  \"topK\": 40\n"
            + "}";
    String project = "YOUR_PROJECT_ID";
    String location = "us-central1";
    String publisher = "google";
    String model = "text-bison@001";

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

  // Get a text prompt from a supported text model
  public static void predictTextPrompt(
      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);

      // Initialize client that will be used to send requests. This client only needs to be created
      // once, and can be reused for multiple requests.
      Value.Builder instanceValue = Value.newBuilder();
      JsonFormat.parser().merge(instance, instanceValue);
      List<Value> instances = new ArrayList<>();
      instances.add(instanceValue.build());

      // Use Value.Builder to convert instance 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);
    }
  }
}

Ruby

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

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

require "google/cloud/ai_platform/v1"

##
# Vertex AI Predict Text Prompt
#
# @param project_id [String] Your Google Cloud project (e.g. "my-project")
# @param location_id [String] Your Processor Location (e.g. "us-central1")
# @param publisher [String] The Model Publisher (e.g. "google")
# @param model [String] The Model Identifier (e.g. "text-bison@001")
#
def predict_text_prompt project_id:, location_id:, publisher:, model:
  # Create the Vertex AI client.
  client = ::Google::Cloud::AIPlatform::V1::PredictionService::Client.new do |config|
    config.endpoint = "#{location_id}-aiplatform.googleapis.com"
  end

  # Build the resource name from the project.
  endpoint = client.endpoint_path(
    project: project_id,
    location: location_id,
    publisher: publisher,
    model: model
  )

  prompt = "Give me ten interview questions for the role of program manager."

  # Initialize the request arguments
  instance = Google::Protobuf::Value.new(
    struct_value: Google::Protobuf::Struct.new(
      fields: {
        "prompt" => Google::Protobuf::Value.new(
          string_value: prompt
        )
      }
    )
  )

  instances = [instance]

  parameters = Google::Protobuf::Value.new(
    struct_value: Google::Protobuf::Struct.new(
      fields: {
        "temperature" => Google::Protobuf::Value.new(number_value: 0.2),
        "maxOutputTokens" => Google::Protobuf::Value.new(number_value: 256),
        "topP" => Google::Protobuf::Value.new(number_value: 0.95),
        "topK" => Google::Protobuf::Value.new(number_value: 40)
      }
    )
  )

  # Make the prediction request
  response = client.predict endpoint: endpoint, instances: instances, parameters: parameters

  # Handle the prediction response
  puts "Predict Response"
  puts response
end

Langkah berikutnya

Untuk menelusuri dan memfilter contoh kode untuk produk Google Cloud lainnya, lihat Google Cloud browser contoh.