Aufforderungen zum Chatten über Code erstellen (Generative Ai)

Erstellen Sie Aufforderungen zur Arbeit mit einem Publisher-Code-Chat-Modell, um eine Chatbot-Unterhaltung zu Code zu führen.

Codebeispiel

C#

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

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

using Google.Cloud.AIPlatform.V1;
using System;
using System.Collections.Generic;
using System.Linq;
using Value = Google.Protobuf.WellKnownTypes.Value;

public class PredictCodeChatSample
{
    public string PredictCodeChat(
        string projectId = "your-project-id",
        string locationId = "us-central1",
        string publisher = "google",
        string model = "codechat-bison@001"
    )
    {
        // Initialize client that will be used to send requests.
        // This client only needs to be created once,
        // and can be reused for multiple requests.
        var client = new PredictionServiceClientBuilder
        {
            Endpoint = $"{locationId}-aiplatform.googleapis.com"
        }.Build();

        // Configure the parent resource.
        var endpoint = EndpointName.FromProjectLocationPublisherModel(projectId, locationId, publisher, model);

        var instance = new Value
        {
            StructValue = new()
            {
                Fields =
                {
                    ["messages"] = Value.ForList(
                        Value.ForStruct(new()
                        {
                            Fields =
                            {
                                ["author"] = Value.ForString("user"),
                                ["content"] = Value.ForString("Hi, how are you?"),
                            }
                        }),
                        Value.ForStruct(new()
                        {
                            Fields =
                            {
                                ["author"] = Value.ForString("system"),
                                ["content"] = Value.ForString("I am doing good. What can I help you in the coding world?"),
                            }
                        }),
                        Value.ForStruct(new()
                        {
                            Fields =
                            {
                                ["author"] = Value.ForString("user"),
                                ["content"] = Value.ForString("Please help write a C# function to calculate the min of two numbers."),
                            }
                        }))
                }
            }
        };

        var instances = new List<Value>
        {
            instance
        };

        var parameters = Value.ForStruct(new()
        {
            Fields =
            {
                { "temperature", new Value { NumberValue = 0.3 } },
                { "maxOutputTokens", new Value { NumberValue = 1024 } }
            }
        });

        // Make the request.
        var response = client.Predict(endpoint, instances, parameters);

        // Parse and return the content.
        var content = response.Predictions.First().StructValue.Fields["candidates"].ListValue.Values[0].StructValue.Fields["content"].StringValue;
        Console.WriteLine($"Content: {content}");
        return content;
    }
}

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.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.InvalidProtocolBufferException;
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 PredictCodeChatSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace this variable before running the sample.
    String project = "YOUR_PROJECT_ID";

    // Learn more about creating prompts to work with a code chat model at:
    // https://cloud.google.com/vertex-ai/docs/generative-ai/code/code-chat-prompts
    String instance =
        "{ \"messages\": [\n"
            + "{\n"
            + "  \"author\": \"user\",\n"
            + "  \"content\": \"Hi, how are you?\"\n"
            + "},\n"
            + "{\n"
            + "  \"author\": \"system\",\n"
            + "  \"content\": \"I am doing good. What can I help you in the coding world?\"\n"
            + " },\n"
            + "{\n"
            + "  \"author\": \"user\",\n"
            + "  \"content\":\n"
            + "     \"Please help write a function to calculate the min of two numbers.\"\n"
            + "}\n"
            + "]}";
    String parameters = "{\n" + "  \"temperature\": 0.5,\n" + "  \"maxOutputTokens\": 1024\n" + "}";
    String location = "us-central1";
    String publisher = "google";
    String model = "codechat-bison@001";

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

  // Use a code chat model to generate a code function
  public static void predictCodeChat(
      String instance,
      String parameters,
      String project,
      String location,
      String publisher,
      String model)
      throws IOException {
    final 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);

      Value instanceValue = stringToValue(instance);
      List<Value> instances = new ArrayList<>();
      instances.add(instanceValue);

      Value parameterValue = stringToValue(parameters);

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

  // Convert a Json string to a protobuf.Value
  static Value stringToValue(String value) throws InvalidProtocolBufferException {
    Value.Builder builder = Value.newBuilder();
    JsonFormat.parser().merge(value, builder);
    return builder.build();
  }
}

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 project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const aiplatform = require('@google-cloud/aiplatform');

// Imports the Google Cloud Prediction service client
const {PredictionServiceClient} = aiplatform.v1;

// Import the helper module for converting arbitrary protobuf.Value objects.
const {helpers} = aiplatform;

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};
const publisher = 'google';
const model = 'codechat-bison@001';

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

async function callPredict() {
  // Configure the parent resource
  const endpoint = `projects/${project}/locations/${location}/publishers/${publisher}/models/${model}`;

  // Learn more about creating prompts to work with a code chat model at:
  // https://cloud.google.com/vertex-ai/docs/generative-ai/code/code-chat-prompts
  const prompt = {
    messages: [
      {
        author: 'user',
        content: 'Hi, how are you?',
      },
      {
        author: 'system',
        content: 'I am doing good. What can I help you in the coding world?',
      },
      {
        author: 'user',
        content:
          'Please help write a function to calculate the min of two numbers',
      },
    ],
  };
  const instanceValue = helpers.toValue(prompt);
  const instances = [instanceValue];

  const parameter = {
    temperature: 0.5,
    maxOutputTokens: 1024,
  };
  const parameters = helpers.toValue(parameter);

  const request = {
    endpoint,
    instances,
    parameters,
  };

  // Predict request
  const [response] = await predictionServiceClient.predict(request);
  console.log('Get code chat response');
  const predictions = response.predictions;
  console.log('\tPredictions :');
  for (const prediction of predictions) {
    console.log(`\t\tPrediction : ${JSON.stringify(prediction)}`);
  }
}

callPredict();

Nächste Schritte

Informationen zum Suchen und Filtern von Codebeispielen für andere Google Cloud-Produkte finden Sie im Google Cloud-Beispielbrowser.