您好，Amazon Bedrock

package main

import (
	"context"
	"encoding/json"
	"flag"
	"fmt"
	"log"
	"os"
	"strings"

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

// Each model provider defines their own individual request and response formats.
// For the format, ranges, and default values for the different models, refer to:
// https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html

type ClaudeRequest struct {
	Prompt            string `json:"prompt"`
	MaxTokensToSample int    `json:"max_tokens_to_sample"`
	// Omitting optional request parameters
}

type ClaudeResponse struct {
	Completion string `json:"completion"`
}

// main uses the AWS SDK for Go (v2) to create an Amazon Bedrock Runtime client
// and invokes Anthropic Claude 2 inside your account and the chosen region.
// This example uses the default settings specified in your shared credentials
// and config files.
func main() {

	region := flag.String("region", "us-east-1", "The AWS region")
	flag.Parse()

	fmt.Printf("Using AWS region: %s\n", *region)

	ctx := context.Background()
	sdkConfig, err := config.LoadDefaultConfig(ctx, config.WithRegion(*region))
	if err != nil {
		fmt.Println("Couldn't load default configuration. Have you set up your AWS account?")
		fmt.Println(err)
		return
	}

	client := bedrockruntime.NewFromConfig(sdkConfig)

	modelId := "anthropic.claude-v2"

	prompt := "Hello, how are you today?"

	// Anthropic Claude requires you to enclose the prompt as follows:
	prefix := "Human: "
	postfix := "\n\nAssistant:"
	wrappedPrompt := prefix + prompt + postfix

	request := ClaudeRequest{
		Prompt:            wrappedPrompt,
		MaxTokensToSample: 200,
	}

	body, err := json.Marshal(request)
	if err != nil {
		log.Panicln("Couldn't marshal the request: ", err)
	}

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

	if err != nil {
		errMsg := err.Error()
		if strings.Contains(errMsg, "no such host") {
			fmt.Printf("Error: The Bedrock service is not available in the selected region. Please double-check the service availability for your region at https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/.\n")
		} else if strings.Contains(errMsg, "Could not resolve the foundation model") {
			fmt.Printf("Error: Could not resolve the foundation model from model identifier: \"%v\". Please verify that the requested model exists and is accessible within the specified region.\n", modelId)
		} else {
			fmt.Printf("Error: Couldn't invoke Anthropic Claude. Here's why: %v\n", err)
		}
		os.Exit(1)
	}

	var response ClaudeResponse

	err = json.Unmarshal(result.Body, &response)

	if err != nil {
		log.Fatal("failed to unmarshal", err)
	}
	fmt.Println("Prompt:\n", prompt)
	fmt.Println("Response from Anthropic Claude:\n", response.Completion)
}

/**
 * @typedef {Object} Content
 * @property {string} text
 *
 * @typedef {Object} Usage
 * @property {number} input_tokens
 * @property {number} output_tokens
 *
 * @typedef {Object} ResponseBody
 * @property {Content[]} content
 * @property {Usage} usage
 */

import { fileURLToPath } from "node:url";
import {
  BedrockRuntimeClient,
  InvokeModelCommand,
} from "@aws-sdk/client-bedrock-runtime";

const AWS_REGION = "us-east-1";

const MODEL_ID = "anthropic.claude-3-haiku-20240307-v1:0";
const PROMPT = "Hi. In a short paragraph, explain what you can do.";

const hello = async () => {
  console.log("=".repeat(35));
  console.log("Welcome to the Amazon Bedrock demo!");
  console.log("=".repeat(35));

  console.log("Model: Anthropic Claude 3 Haiku");
  console.log(`Prompt: ${PROMPT}\n`);
  console.log("Invoking model...\n");

  // Create a new Bedrock Runtime client instance.
  const client = new BedrockRuntimeClient({ region: AWS_REGION });

  // Prepare the payload for the model.
  const payload = {
    anthropic_version: "bedrock-2023-05-31",
    max_tokens: 1000,
    messages: [{ role: "user", content: [{ type: "text", text: PROMPT }] }],
  };

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

  // Decode and return the response(s)
  const decodedResponseBody = new TextDecoder().decode(apiResponse.body);
  /** @type {ResponseBody} */
  const responseBody = JSON.parse(decodedResponseBody);
  const responses = responseBody.content;

  if (responses.length === 1) {
    console.log(`Response: ${responses[0].text}`);
  } else {
    console.log("Haiku returned multiple responses:");
    console.log(responses);
  }

  console.log(`\nNumber of input tokens:   ${responseBody.usage.input_tokens}`);
  console.log(`Number of output tokens: ${responseBody.usage.output_tokens}`);
};

if (process.argv[1] === fileURLToPath(import.meta.url)) {
  await hello();
}

使用 InvokeModel 操作傳送提示至模型。

"""
Uses the Amazon Bedrock runtime client InvokeModel operation to send a prompt to a model.
"""
import logging
import json
import boto3


from botocore.exceptions import ClientError


logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def invoke_model(brt, model_id, prompt):
    """
    Invokes the specified model with the supplied prompt.
    param brt: A bedrock runtime boto3 client
    param model_id: The model ID for the model that you want to use.
    param prompt: The prompt that you want to send to the model.

    :return: The text response from the model.
    """

    # Format the request payload using the model's native structure.
    native_request = {
        "inputText": prompt,
        "textGenerationConfig": {
            "maxTokenCount": 512,
            "temperature": 0.5,
            "topP": 0.9
        }
    }

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

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

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

        # Extract and print the response text.
        response_text = model_response["results"][0]["outputText"]
        return response_text

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


def main():
    """Entry point for the example. Uses the AWS SDK for Python (Boto3)
    to create an Amazon Bedrock runtime client. Then sends a prompt to a model
    in the region set in the callers profile and credentials.
    """

    # Create an Amazon Bedrock Runtime client.
    brt = boto3.client("bedrock-runtime")

    # Set the model ID, e.g., Amazon Titan Text G1 - Express.
    model_id = "amazon.titan-text-express-v1"

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

    # Send the prompt to the model.
    response = invoke_model(brt, model_id, prompt)

    print(f"Response: {response}")

    logger.info("Done.")


if __name__ == "__main__":
    main()

使用 Converse 操作將使用者訊息傳送至模型。

"""
Uses the Amazon Bedrock runtime client Converse operation to send a user message to a model.
"""
import logging
import boto3

from botocore.exceptions import ClientError


logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def converse(brt, model_id, user_message):
    """
    Uses the Converse operation to send a user message to the supplied model.
    param brt: A bedrock runtime boto3 client
    param model_id: The model ID for the model that you want to use.
    param user message: The user message that you want to send to the model.

    :return: The text response from the model.
    """

    # Format the request payload using the model's native structure.
    conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

    try:
        # Send the message to the model, using a basic inference configuration.
        response = brt.converse(
            modelId=model_id,
            messages=conversation,
            inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
        )

        # Extract and print the response text.
        response_text = response["output"]["message"]["content"][0]["text"]
        return response_text

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


def main():
    """Entry point for the example. Uses the AWS SDK for Python (Boto3)
    to create an Amazon Bedrock runtime client. Then sends a user message to a model
    in the region set in the callers profile and credentials.
    """

    # Create an Amazon Bedrock Runtime client.
    brt = boto3.client("bedrock-runtime")

    # Set the model ID, e.g., Amazon Titan Text G1 - Express.
    model_id = "amazon.titan-text-express-v1"

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

    # Send the message to the model.
    response = converse(brt, model_id, message)

    print(f"Response: {response}")

    logger.info("Done.")


if __name__ == "__main__":
    main()