Chat-Prompts testen (Generative AI)

Testen Sie einen Text-Prompt mithilfe eines Publisher-Chatmodells.

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.v1beta1.EndpointName;
import com.google.cloud.aiplatform.v1beta1.PredictResponse;
import com.google.cloud.aiplatform.v1beta1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1beta1.PredictionServiceSettings;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

// Send a Predict request to a large language model to test a chat prompt
public class PredictChatPromptSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String instance =
        "{\n"
            + "   \"context\":  \"My name is Ned. You are my personal assistant. My favorite movies"
            + " are Lord of the Rings and Hobbit.\",\n"
            + "   \"examples\": [ { \n"
            + "       \"input\": {\"content\": \"Who do you work for?\"},\n"
            + "       \"output\": {\"content\": \"I work for Ned.\"}\n"
            + "    },\n"
            + "    { \n"
            + "       \"input\": {\"content\": \"What do I like?\"},\n"
            + "       \"output\": {\"content\": \"Ned likes watching movies.\"}\n"
            + "    }],\n"
            + "   \"messages\": [\n"
            + "    { \n"
            + "       \"author\": \"user\",\n"
            + "       \"content\": \"Are my favorite movies based on a book series?\"\n"
            + "    }]\n"
            + "}";
    String parameters =
        "{\n"
            + "  \"temperature\": 0.3,\n"
            + "  \"maxDecodeSteps\": 200,\n"
            + "  \"topP\": 0.8,\n"
            + "  \"topK\": 40\n"
            + "}";
    String project = "YOUR_PROJECT_ID";
    String publisher = "google";
    String model = "chat-bison@001";

    predictChatPrompt(instance, parameters, project, publisher, model);
  }

  static void predictChatPrompt(
      String instance, String parameters, String project, String publisher, String model)
      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.
    try (PredictionServiceClient predictionServiceClient =
        PredictionServiceClient.create(predictionServiceSettings)) {
      String location = "us-central1";
      final EndpointName endpointName =
          EndpointName.ofProjectLocationPublisherModelName(project, location, publisher, model);

      Value.Builder instanceValue = Value.newBuilder();
      JsonFormat.parser().merge(instance, instanceValue);
      List<Value> instances = new ArrayList<>();
      instances.add(instanceValue.build());

      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");
    }
  }
}

Nächste Schritte

Wenn Sie nach Codebeispielen für andere Google Cloud -Produkte suchen und filtern möchten, können Sie den Google Cloud -Beispielbrowser verwenden.