Menguji perintah chat (Generative AI)

Menguji perintah teks menggunakan model chat penayang.

Jelajahi lebih lanjut

Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:

Contoh kode

C#

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

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


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

public class PredictChatPromptSample
{
    public string PredictChatPrompt(
        string projectId = "your-project-id",
        string locationId = "us-central1",
        string publisher = "google",
        string model = "chat-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);

        // Initialize request argument(s).
        var prompt = "How many planets are there in the solar system?";

        // You can construct Protobuf from JSON.
        var instanceJson = JsonConvert.SerializeObject(new
        {
            context = "My name is Miles. You are an astronomer, knowledgeable about the solar system.",
            examples = new[]
            {
                new
                {
                    input = new { content = "How many moons does Mars have?" },
                    output = new { content = "The planet Mars has two moons, Phobos and Deimos." }
                }
            },
            messages = new[]
            {
                new
                {
                    author = "user",
                    content = prompt
                }
            }
        });
        var instance = Value.Parser.ParseJson(instanceJson);

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

        // You can construct Protobuf from JSON.
        var parametersJson = JsonConvert.SerializeObject(new
        {
            temperature = 0.3,
            maxDecodeSteps = 200,
            topP = 0.8,
            topK = 40
        });
        var parameters = Value.Parser.ParseJson(parametersJson);

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

        // Parse the response 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

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, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 


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");
    }
  }
}

Node.js

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

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

/**
 * 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 = 'chat-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}`;

  const prompt = {
    context:
      'My name is Miles. You are an astronomer, knowledgeable about the solar system.',
    examples: [
      {
        input: {content: 'How many moons does Mars have?'},
        output: {
          content: 'The planet Mars has two moons, Phobos and Deimos.',
        },
      },
    ],
    messages: [
      {
        author: 'user',
        content: 'How many planets are there in the solar system?',
      },
    ],
  };
  const instanceValue = helpers.toValue(prompt);
  const instances = [instanceValue];

  const parameter = {
    temperature: 0.2,
    maxOutputTokens: 256,
    topP: 0.95,
    topK: 40,
  };
  const parameters = helpers.toValue(parameter);

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

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

callPredict();

Python

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

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

from vertexai.language_models import ChatModel, InputOutputTextPair

def science_tutoring(temperature: float = 0.2) -> None:
    chat_model = ChatModel.from_pretrained("chat-bison@001")

    # TODO developer - override these parameters as needed:
    parameters = {
        "temperature": temperature,  # Temperature controls the degree of randomness in token selection.
        "max_output_tokens": 256,  # Token limit determines the maximum amount of text output.
        "top_p": 0.95,  # Tokens are selected from most probable to least until the sum of their probabilities equals the top_p value.
        "top_k": 40,  # A top_k of 1 means the selected token is the most probable among all tokens.
    }

    chat = chat_model.start_chat(
        context="My name is Miles. You are an astronomer, knowledgeable about the solar system.",
        examples=[
            InputOutputTextPair(
                input_text="How many moons does Mars have?",
                output_text="The planet Mars has two moons, Phobos and Deimos.",
            ),
        ],
    )

    response = chat.send_message(
        "How many planets are there in the solar system?", **parameters
    )
    print(f"Response from Model: {response.text}")

    return response

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