Create prompts to generate code (Generative AI)

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

public class PredictCodeGenerationFunctionSample
{
    public string PredictFunction(
        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 instances = new List<Value>
        {
            Value.ForStruct(new()
            {
                Fields =
                {
                    ["prefix"] = Value.ForString("Write a C# function that checks if a year is a leap year."),
                }
            })
        };

        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;
    }
}

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 PredictCodeGenerationFunctionSample {

  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 function that checks if a year is a leap year.\"}";
    String parameters = "{\n" + "  \"temperature\": 0.5,\n" + "  \"maxOutputTokens\": 256,\n" + "}";
    String location = "us-central1";
    String publisher = "google";
    String model = "code-bison@001";

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

  // Use Codey for Code Generation to generate a code function
  public static void predictFunction(
      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();
  }
}

/**
 * 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 function that checks if a year is a leap year.',
  };
  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();