Richiama modelli Anthropic Claude su Amazon Bedrock utilizzando l'API Invoke Model con un flusso di risposta - Amazon Bedrock

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Richiama modelli Anthropic Claude su Amazon Bedrock utilizzando l'API Invoke Model con un flusso di risposta

I seguenti esempi di codice mostrano come inviare un messaggio di testo ai modelli Anthropic Claude, utilizzando l'API Invoke Model, e stampare il flusso di risposta.

.NET
AWS SDK for .NET
Nota

C'è altro su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Utilizza l'API Invoke Model per inviare un messaggio di testo ed elaborare il flusso di risposta in tempo reale.

// Use the native inference API to send a text message to Anthropic Claude
// and print the response stream.

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., Claude 3 Haiku.
var modelId = "anthropic.claude-3-haiku-20240307-v1:0";

// 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
{
    anthropic_version = "bedrock-2023-05-31",
    max_tokens = 512,
    temperature = 0.5,
    messages = new[]
    {
        new { role = "user", content = userMessage }
    }
});

// Create a request with the model ID, the user message, and an inference configuration.
var request = new InvokeModelWithResponseStreamRequest()
{
    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 streamingResponse = await client.InvokeModelWithResponseStreamAsync(request);

    // Extract and print the streamed response text in real-time.
    foreach (var item in streamingResponse.Body)
    {
        var chunk = JsonSerializer.Deserialize<JsonObject>((item as PayloadPart).Bytes);
        var text = chunk["delta"]?["text"] ?? "";
        Console.Write(text);
    }
}
catch (AmazonBedrockRuntimeException e)
{
    Console.WriteLine($"ERROR: Can't invoke '{modelId}'. Reason: {e.Message}");
    throw;
}
Go
SDK per Go V2
Nota

C'è altro su GitHub. Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Utilizza l'API Invoke Model per inviare un messaggio di testo ed elaborare il flusso di risposta in tempo reale.


// 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 Request struct {
	Prompt            string  `json:"prompt"`
	MaxTokensToSample int     `json:"max_tokens_to_sample"`
	Temperature       float64 `json:"temperature,omitempty"`
}

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

// Invokes Anthropic Claude on Amazon Bedrock to run an inference and asynchronously
// process the response stream.

func (wrapper InvokeModelWithResponseStreamWrapper) InvokeModelWithResponseStream(prompt string) (string, error) {

	modelId := "anthropic.claude-v2"

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

	request := ClaudeRequest{
		Prompt:            prompt,
		MaxTokensToSample: 200,
		Temperature:       0.5,
		StopSequences:     []string{"\n\nHuman:"},
	}

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

	output, err := wrapper.BedrockRuntimeClient.InvokeModelWithResponseStream(context.Background(), &bedrockruntime.InvokeModelWithResponseStreamInput{
		Body:        body,
		ModelId:     aws.String(modelId),
		ContentType: aws.String("application/json"),
	})

	if err != nil {
		errMsg := err.Error()
		if strings.Contains(errMsg, "no such host") {
			log.Printf("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") {
			log.Printf("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 {
			log.Printf("Couldn't invoke Anthropic Claude. Here's why: %v\n", err)
		}
	}

	resp, err := processStreamingOutput(output, func(ctx context.Context, part []byte) error {
		fmt.Print(string(part))
		return nil
	})

	if err != nil {
		log.Fatal("streaming output processing error: ", err)
	}

	return resp.Completion, nil

}

type StreamingOutputHandler func(ctx context.Context, part []byte) error

func processStreamingOutput(output *bedrockruntime.InvokeModelWithResponseStreamOutput, handler StreamingOutputHandler) (Response, error) {

	var combinedResult string
	resp := Response{}

	for event := range output.GetStream().Events() {
		switch v := event.(type) {
		case *types.ResponseStreamMemberChunk:

			//fmt.Println("payload", string(v.Value.Bytes))

			var resp Response
			err := json.NewDecoder(bytes.NewReader(v.Value.Bytes)).Decode(&resp)
			if err != nil {
				return resp, err
			}

			err = handler(context.Background(), []byte(resp.Completion))
			if err != nil {
				return resp, err
			}

			combinedResult += resp.Completion

		case *types.UnknownUnionMember:
			fmt.Println("unknown tag:", v.Tag)

		default:
			fmt.Println("union is nil or unknown type")
		}
	}

	resp.Completion = combinedResult

	return resp, nil
}
Java
SDK per Java 2.x
Nota

C'è altro su GitHub. Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Utilizza l'API Invoke Model per inviare un messaggio di testo ed elaborare il flusso di risposta in tempo reale.

// Use the native inference API to send a text message to Anthropic Claude
// and print the response stream.

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.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamRequest;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler;

import java.util.Objects;
import java.util.concurrent.ExecutionException;

import static software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler.Visitor;

public class InvokeModelWithResponseStream {

    public static String invokeModelWithResponseStream() throws ExecutionException, InterruptedException {

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

        // Set the model ID, e.g., Claude 3 Haiku.
        var modelId = "anthropic.claude-3-haiku-20240307-v1:0";

        // The InvokeModelWithResponseStream 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-anthropic-claude-messages.html
        var nativeRequestTemplate = """
                {
                    "anthropic_version": "bedrock-2023-05-31",
                    "max_tokens": 512,
                    "temperature": 0.5,
                    "messages": [{
                        "role": "user",
                        "content": "{{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);

        // Create a request with the model ID and the model's native request payload.
        var request = InvokeModelWithResponseStreamRequest.builder()
                .body(SdkBytes.fromUtf8String(nativeRequest))
                .modelId(modelId)
                .build();

        // Prepare a buffer to accumulate the generated response text.
        var completeResponseTextBuffer = new StringBuilder();

        // Prepare a handler to extract, accumulate, and print the response text in real-time.
        var responseStreamHandler = InvokeModelWithResponseStreamResponseHandler.builder()
                .subscriber(Visitor.builder().onChunk(chunk -> {
                    var response = new JSONObject(chunk.bytes().asUtf8String());

                    // Extract and print the text from the content blocks.
                    if (Objects.equals(response.getString("type"), "content_block_delta")) {
                        var text = new JSONPointer("/delta/text").queryFrom(response);
                        System.out.print(text);

                        // Append the text to the response text buffer.
                        completeResponseTextBuffer.append(text);
                    }
                }).build()).build();

        try {
            // Send the request and wait for the handler to process the response.
            client.invokeModelWithResponseStream(request, responseStreamHandler).get();

            // Return the complete response text.
            return completeResponseTextBuffer.toString();

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        invokeModelWithResponseStream();
    }
}
JavaScript
SDK per JavaScript (v3)
Nota

C'è altro da fare. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Utilizza l'API Invoke Model per inviare un messaggio di testo ed elaborare il flusso di risposta in tempo reale.

// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: Apache-2.0

import { fileURLToPath } from "url";

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

/**
 * @typedef {Object} ResponseContent
 * @property {string} text
 *
 * @typedef {Object} MessagesResponseBody
 * @property {ResponseContent[]} content
 *
 * @typedef {Object} Delta
 * @property {string} text
 *
 * @typedef {Object} Message
 * @property {string} role
 *
 * @typedef {Object} Chunk
 * @property {string} type
 * @property {Delta} delta
 * @property {Message} message
 */

/**
 * Invokes Anthropic Claude 3 using the Messages API.
 *
 * To learn more about the Anthropic Messages API, go to:
 * https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
 *
 * @param {string} prompt - The input text prompt for the model to complete.
 * @param {string} [modelId] - The ID of the model to use. Defaults to "anthropic.claude-3-haiku-20240307-v1:0".
 */
export const invokeModel = async (
  prompt,
  modelId = "anthropic.claude-3-haiku-20240307-v1:0",
) => {
  // Create a new Bedrock Runtime client instance.
  const client = new BedrockRuntimeClient({ region: "us-east-1" });

  // 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 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 {MessagesResponseBody} */
  const responseBody = JSON.parse(decodedResponseBody);
  return responseBody.content[0].text;
};

/**
 * Invokes Anthropic Claude 3 and processes the response stream.
 *
 * To learn more about the Anthropic Messages API, go to:
 * https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
 *
 * @param {string} prompt - The input text prompt for the model to complete.
 * @param {string} [modelId] - The ID of the model to use. Defaults to "anthropic.claude-3-haiku-20240307-v1:0".
 */
export const invokeModelWithResponseStream = async (
  prompt,
  modelId = "anthropic.claude-3-haiku-20240307-v1:0",
) => {
  // Create a new Bedrock Runtime client instance.
  const client = new BedrockRuntimeClient({ region: "us-east-1" });

  // 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 API to respond.
  const command = new InvokeModelWithResponseStreamCommand({
    contentType: "application/json",
    body: JSON.stringify(payload),
    modelId,
  });
  const apiResponse = await client.send(command);

  let completeMessage = "";

  // Decode and process the response stream
  for await (const item of apiResponse.body) {
    /** @type Chunk */
    const chunk = JSON.parse(new TextDecoder().decode(item.chunk.bytes));
    const chunk_type = chunk.type;

    if (chunk_type === "content_block_delta") {
      const text = chunk.delta.text;
      completeMessage = completeMessage + text;
      process.stdout.write(text);
    }
  }

  // Return the final response
  return completeMessage;
};

// Invoke the function if this file was run directly.
if (process.argv[1] === fileURLToPath(import.meta.url)) {
  const prompt = 'Write a paragraph starting with: "Once upon a time..."';
  const modelId = FoundationModels.CLAUDE_3_HAIKU.modelId;
  console.log(`Prompt: ${prompt}`);
  console.log(`Model ID: ${modelId}`);

  try {
    console.log("-".repeat(53));
    const response = await invokeModel(prompt, modelId);
    console.log("\n" + "-".repeat(53));
    console.log("Final structured response:");
    console.log(response);
  } catch (err) {
    console.log(`\n${err}`);
  }
}
Python
SDK per Python (Boto3)
Nota

C'è altro su GitHub. Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Utilizza l'API Invoke Model per inviare un messaggio di testo ed elaborare il flusso di risposta in tempo reale.

# Use the native inference API to send a text message to Anthropic Claude
# and print the response stream.

import boto3
import json

# 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., Claude 3 Haiku.
model_id = "anthropic.claude-3-haiku-20240307-v1:0"

# 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 = {
    "anthropic_version": "bedrock-2023-05-31",
    "max_tokens": 512,
    "temperature": 0.5,
    "messages": [
        {
            "role": "user",
            "content": [{"type": "text", "text": prompt}],
        }
    ],
}

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

# Invoke the model with the request.
streaming_response = client.invoke_model_with_response_stream(
    modelId=model_id, body=request
)

# Extract and print the response text in real-time.
for event in streaming_response["body"]:
    chunk = json.loads(event["chunk"]["bytes"])
    if chunk["type"] == "content_block_delta":
        print(chunk["delta"].get("text", ""), end="")

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