Crea mensajes para generar pruebas de unidades (IA generativa)

Crea mensajes que funcionen con un modelo de chat del publicador para generar pruebas de unidades.

Muestra de código

C#

Antes de probar este ejemplo, sigue las instrucciones de configuración para C# incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI C#.

Para autenticarte en Vertex AI, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local.


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

public class PredictCodeGenerationUnitTestSample
{
    public string PredictUnitTest(
        string projectId = "your-project-id",
        string locationId = "us-central1",
        string publisher = "google",
        string model = "code-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 prefix = @"
Write a unit test for this function:
    public static bool IsLeapYear(int year)
    {
        if (year % 4 == 0)
        {
            if (year % 100 == 0)
            {
                if (year % 400 == 0)
                {
                    return true;
                }
                else
                {
                    return false;
                }
            }
            else
            {
                return true;
            }
        }
        else
        {
            return false;
        }
    }";

        var instances = new List<Value>
        {
            Value.ForStruct(new()
            {
                Fields =
                {
                    ["prefix"] = Value.ForString(prefix),
                }
            })
        };

        var parameters = Value.ForStruct(new()
        {
            Fields =
            {
                { "temperature", new Value { NumberValue = 0.5 } },
                { "maxOutputTokens", new Value { NumberValue = 256 } }
            }
        });

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

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

Java

Antes de probar este ejemplo, sigue las instrucciones de configuración para Java incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Java.


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 PredictCodeGenerationUnitTestSample {

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

    // Learn how to create prompts to work with a code model to generate code:
    // https://cloud.google.com/vertex-ai/docs/generative-ai/code/code-generation-prompts
    String instance =
        "{ \"prefix\": \"Write a unit test for this function:\n"
            + "    def is_leap_year(year):\n"
            + "        if year % 4 == 0:\n"
            + "            if year % 100 == 0:\n"
            + "                if year % 400 == 0:\n"
            + "                    return True\n"
            + "                else:\n"
            + "                    return False\n"
            + "            else:\n"
            + "                return True\n"
            + "        else:\n"
            + "            return False\n"
            + "\"}";
    String parameters = "{\n" + "  \"temperature\": 0.5,\n" + "  \"maxOutputTokens\": 256\n" + "}";
    String location = "us-central1";
    String publisher = "google";
    String model = "code-bison@001";

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

  // Use Codey for Code Generation to generate a unit test
  public static void predictUnitTest(
      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

Antes de probar este ejemplo, sigue las instrucciones de configuración para Node.js incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Node.js.

/**
 * 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 = 'code-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 = {
    prefix:
      'Write a unit test for this function: \
    def is_leap_year(year): \
        if year % 4 == 0: \
            if year % 100 == 0: \
                if year % 400 == 0: \
                    return True \
                else: \
                    return False \
            else: \
                return True \
        else: \
            return False',
  };
  const instanceValue = helpers.toValue(prompt);
  const instances = [instanceValue];

  const parameter = {
    temperature: 0.5,
    maxOutputTokens: 256,
  };
  const parameters = helpers.toValue(parameter);

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

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

callPredict();

Python

Antes de probar este ejemplo, sigue las instrucciones de configuración para Python incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Python.

import textwrap

from vertexai.language_models import CodeGenerationModel

# TODO developer - override these parameters as needed:
parameters = {
    "temperature": 0.5,  # Temperature controls the degree of randomness in token selection.
    "max_output_tokens": 256,  # Token limit determines the maximum amount of text output.
}

code_generation_model = CodeGenerationModel.from_pretrained("code-bison@001")
response = code_generation_model.predict(
    prefix=textwrap.dedent(
        """\
Write a unit test for this function:
def is_leap_year(year):
    if year % 4 == 0:
        if year % 100 == 0:
            if year % 400 == 0:
                return True
            else:
                return False
        else:
            return True
    else:
        return False
"""
    ),
    **parameters,
)

print(f"Response from Model: {response.text}")

¿Qué sigue?

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