代码聊天

from vertexai.language_models import CodeChatModel

# TODO developer - override these parameters as needed:
parameters = {
    "temperature": 0.5,  # Temperature controls the degree of randomness in token selection.
    "max_output_tokens": 1024,  # Token limit determines the maximum amount of text output.
}

code_chat_model = CodeChatModel.from_pretrained("codechat-bison@001")
chat_session = code_chat_model.start_chat()

response = chat_session.send_message(
    "Please help write a function to calculate the min of two numbers", **parameters
)
print(f"Response from Model: {response.text}")
# Response from Model: Sure, here is a function that you can use to calculate the minimum of two numbers:
# ```
# def min(a, b):
#   """
#   Calculates the minimum of two numbers.
#   Args:
#     a: The first number.
# ...

/**
 * 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();

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();
  }
}