使用叫用模型 API 在 Amazon Bedrock 上叫用 AI21 實驗室 Jurassic-2 模型

使用叫用模型 API 傳送文字訊息。

// Use the native inference API to send a text message to AI21 Labs Jurassic-2.

using System;
using System.IO;
using System.Text.Json;
using System.Text.Json.Nodes;
using Amazon;
using Amazon.BedrockRuntime;
using Amazon.BedrockRuntime.Model;

// Create a Bedrock Runtime client in the AWS Region you want to use.
var client = new AmazonBedrockRuntimeClient(RegionEndpoint.USEast1);

// Set the model ID, e.g., Jurassic-2 Mid.
var modelId = "ai21.j2-mid-v1";

// Define the user message.
var userMessage = "Describe the purpose of a 'hello world' program in one line.";

//Format the request payload using the model's native structure.
var nativeRequest = JsonSerializer.Serialize(new
{
    prompt = userMessage,
    maxTokens = 512,
    temperature = 0.5
});

// Create a request with the model ID and the model's native request payload.
var request = new InvokeModelRequest()
{
    ModelId = modelId,
    Body = new MemoryStream(System.Text.Encoding.UTF8.GetBytes(nativeRequest)),
    ContentType = "application/json"
};

try
{
    // Send the request to the Bedrock Runtime and wait for the response.
    var response = await client.InvokeModelAsync(request);

    // Decode the response body.
    var modelResponse = await JsonNode.ParseAsync(response.Body);

    // Extract and print the response text.
    var responseText = modelResponse["completions"]?[0]?["data"]?["text"] ?? "";
    Console.WriteLine(responseText);
}
catch (AmazonBedrockRuntimeException e)
{
    Console.WriteLine($"ERROR: Can't invoke '{modelId}'. Reason: {e.Message}");
    throw;
}

使用叫用模型 API 傳送文字訊息。

import (
	"context"
	"encoding/json"
	"log"
	"strings"

	"github.com/aws/aws-sdk-go-v2/aws"
	"github.com/aws/aws-sdk-go-v2/service/bedrockruntime"
)

// InvokeModelWrapper encapsulates Amazon Bedrock actions used in the examples.
// It contains a Bedrock Runtime client that is used to invoke foundation models.
type InvokeModelWrapper struct {
	BedrockRuntimeClient *bedrockruntime.Client
}



// Each model provider has their own individual request and response formats.
// For the format, ranges, and default values for AI21 Labs Jurassic-2, refer to:
// https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-jurassic2.html

type Jurassic2Request struct {
	Prompt      string  `json:"prompt"`
	MaxTokens   int     `json:"maxTokens,omitempty"`
	Temperature float64 `json:"temperature,omitempty"`
}

type Jurassic2Response struct {
	Completions []Completion `json:"completions"`
}
type Completion struct {
	Data Data `json:"data"`
}
type Data struct {
	Text string `json:"text"`
}

// Invokes AI21 Labs Jurassic-2 on Amazon Bedrock to run an inference using the input
// provided in the request body.
func (wrapper InvokeModelWrapper) InvokeJurassic2(ctx context.Context, prompt string) (string, error) {
	modelId := "ai21.j2-mid-v1"

	body, err := json.Marshal(Jurassic2Request{
		Prompt:      prompt,
		MaxTokens:   200,
		Temperature: 0.5,
	})

	if err != nil {
		log.Fatal("failed to marshal", err)
	}

	output, err := wrapper.BedrockRuntimeClient.InvokeModel(ctx, &bedrockruntime.InvokeModelInput{
		ModelId:     aws.String(modelId),
		ContentType: aws.String("application/json"),
		Body:        body,
	})

	if err != nil {
		ProcessError(err, modelId)
	}

	var response Jurassic2Response
	if err := json.Unmarshal(output.Body, &response); err != nil {
		log.Fatal("failed to unmarshal", err)
	}

	return response.Completions[0].Data.Text, nil
}

使用叫用模型 API 傳送文字訊息。

// Use the native inference API to send a text message to AI21 Labs Jurassic-2.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

public class InvokeModel {

    public static String invokeModel() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Jurassic-2 Mid.
        var modelId = "ai21.j2-mid-v1";

        // The InvokeModel API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-jurassic2.html
        var nativeRequestTemplate = "{ \"prompt\": \"{{prompt}}\" }";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in the model's native request payload.
        String nativeRequest = nativeRequestTemplate.replace("{{prompt}}", prompt);

        try {
            // Encode and send the request to the Bedrock Runtime.
            var response = client.invokeModel(request -> request
                    .body(SdkBytes.fromUtf8String(nativeRequest))
                    .modelId(modelId)
            );

            // Decode the response body.
            var responseBody = new JSONObject(response.body().asUtf8String());

            // Retrieve the generated text from the model's response.
            var text = new JSONPointer("/completions/0/data/text").queryFrom(responseBody).toString();
            System.out.println(text);

            return text;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        invokeModel();
    }
}

使用叫用模型 API 傳送文字訊息。

import { fileURLToPath } from "node:url";

import { FoundationModels } from "../../config/foundation_models.js";
import {
  BedrockRuntimeClient,
  InvokeModelCommand,
} from "@aws-sdk/client-bedrock-runtime";

/**
 * @typedef {Object} Data
 * @property {string} text
 *
 * @typedef {Object} Completion
 * @property {Data} data
 *
 * @typedef {Object} ResponseBody
 * @property {Completion[]} completions
 */

/**
 * Invokes an AI21 Labs Jurassic-2 model.
 *
 * @param {string} prompt - The input text prompt for the model to complete.
 * @param {string} [modelId] - The ID of the model to use. Defaults to "ai21.j2-mid-v1".
 */
export const invokeModel = async (prompt, modelId = "ai21.j2-mid-v1") => {
  // Create a new Bedrock Runtime client instance.
  const client = new BedrockRuntimeClient({ region: "us-east-1" });

  // Prepare the payload for the model.
  const payload = {
    prompt,
    maxTokens: 500,
    temperature: 0.5,
  };

  // Invoke the model with the payload and wait for the response.
  const command = new InvokeModelCommand({
    contentType: "application/json",
    body: JSON.stringify(payload),
    modelId,
  });
  const apiResponse = await client.send(command);

  // Decode and return the response(s).
  const decodedResponseBody = new TextDecoder().decode(apiResponse.body);
  /** @type {ResponseBody} */
  const responseBody = JSON.parse(decodedResponseBody);
  return responseBody.completions[0].data.text;
};

// Invoke the function if this file was run directly.
if (process.argv[1] === fileURLToPath(import.meta.url)) {
  const prompt =
    'Complete the following in one sentence: "Once upon a time..."';
  const modelId = FoundationModels.JURASSIC2_MID.modelId;
  console.log(`Prompt: ${prompt}`);
  console.log(`Model ID: ${modelId}`);

  try {
    console.log("-".repeat(53));
    const response = await invokeModel(prompt, modelId);
    console.log(response);
  } catch (err) {
    console.log(err);
  }
}

使用叫用模型 API 傳送文字訊息。

    public function invokeJurassic2($prompt)
    {
        # The different model providers have individual request and response formats.
        # For the format, ranges, and default values for AI21 Labs Jurassic-2, refer to:
        # https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-jurassic2.html

        $completion = "";
        try {
            $modelId = 'ai21.j2-mid-v1';
            $body = [
                'prompt' => $prompt,
                'temperature' => 0.5,
                'maxTokens' => 200,
            ];
            $result = $this->bedrockRuntimeClient->invokeModel([
                'contentType' => 'application/json',
                'body' => json_encode($body),
                'modelId' => $modelId,
            ]);
            $response_body = json_decode($result['body']);
            $completion = $response_body->completions[0]->data->text;
        } catch (Exception $e) {
            echo "Error: ({$e->getCode()}) - {$e->getMessage()}\n";
        }

        return $completion;
    }

使用叫用模型 API 傳送文字訊息。

# Use the native inference API to send a text message to AI21 Labs Jurassic-2.

import boto3
import json

from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Jurassic-2 Mid.
model_id = "ai21.j2-mid-v1"

# Define the prompt for the model.
prompt = "Describe the purpose of a 'hello world' program in one line."

# Format the request payload using the model's native structure.
native_request = {
    "prompt": prompt,
    "maxTokens": 512,
    "temperature": 0.5,
}

# Convert the native request to JSON.
request = json.dumps(native_request)

try:
    # Invoke the model with the request.
    response = client.invoke_model(modelId=model_id, body=request)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)

# Decode the response body.
model_response = json.loads(response["body"].read())

# Extract and print the response text.
response_text = model_response["completions"][0]["data"]["text"]
print(response_text)