Invoque o Meta Llama 3 no Amazon Bedrock usando a API Invoke Model com um fluxo de resposta

Use a API Invoke Model para enviar uma mensagem de texto e imprimir o fluxo de resposta.

// Send a prompt to Meta Llama 3 and print the response stream in real-time.
public class InvokeModelWithResponseStreamQuickstart {

    public static void main(String[] args) {

        // Create a Bedrock Runtime client in the AWS Region of your choice.
        var client = BedrockRuntimeAsyncClient.builder()
                .region(Region.US_WEST_2)
                .build();

        // Set the model ID, e.g., Llama 3 8B Instruct.
        var modelId = "meta.llama3-8b-instruct-v1:0";

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

        // Embed the message in Llama 3's prompt format.
        var prompt = MessageFormat.format("""
                <|begin_of_text|>
                <|start_header_id|>user<|end_header_id|>
                {0}
                <|eot_id|>
                <|start_header_id|>assistant<|end_header_id|>
                """, userMessage);

        // Create a JSON payload using the model's native structure.
        var request = new JSONObject()
                .put("prompt", prompt)
                // Optional inference parameters:
                .put("max_gen_len", 512)
                .put("temperature", 0.5F)
                .put("top_p", 0.9F);

        // Create a handler to extract and print the response text in real-time.
        var streamHandler = InvokeModelWithResponseStreamResponseHandler.builder()
                .subscriber(event -> event.accept(
                        InvokeModelWithResponseStreamResponseHandler.Visitor.builder()
                                .onChunk(c -> {
                                    var chunk = new JSONObject(c.bytes().asUtf8String());
                                    if (chunk.has("generation")) {
                                        System.out.print(chunk.getString("generation"));
                                    }
                                }).build())
                ).build();

        // Encode and send the request. Let the stream handler process the response.
        client.invokeModelWithResponseStream(req -> req
                .body(SdkBytes.fromUtf8String(request.toString()))
                .modelId(modelId), streamHandler
        ).join();
    }
}
// Learn more about the Llama 3 prompt format at:
// https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3

Use a API Invoke Model para enviar uma mensagem de texto e imprimir o fluxo de resposta.

// Send a prompt to Meta Llama 3 and print the response stream in real-time.

import {
  BedrockRuntimeClient,
  InvokeModelWithResponseStreamCommand,
} from "@aws-sdk/client-bedrock-runtime";

// Create a Bedrock Runtime client in the AWS Region of your choice.
const client = new BedrockRuntimeClient({ region: "us-west-2" });

// Set the model ID, e.g., Llama 3 8B Instruct.
const modelId = "meta.llama3-8b-instruct-v1:0";

// Define the user message to send.
const userMessage =
  "Describe the purpose of a 'hello world' program in one sentence.";

// Embed the message in Llama 3's prompt format.
const prompt = `
<|begin_of_text|>
<|start_header_id|>user<|end_header_id|>
${userMessage}
<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
`;

// Format the request payload using the model's native structure.
const request = {
  prompt,
  // Optional inference parameters:
  max_gen_len: 512,
  temperature: 0.5,
  top_p: 0.9,
};

// Encode and send the request.
const responseStream = await client.send(
  new InvokeModelWithResponseStreamCommand({
    contentType: "application/json",
    body: JSON.stringify(request),
    modelId,
  }),
);

// Extract and print the response stream in real-time.
for await (const event of responseStream.body) {
  /** @type {{ generation: string }} */
  const chunk = JSON.parse(new TextDecoder().decode(event.chunk.bytes));
  if (chunk.generation) {
    process.stdout.write(chunk.generation);
  }
}

// Learn more about the Llama 3 prompt format at:
// https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3

Use a API Invoke Model para enviar uma mensagem de texto e imprimir o fluxo de resposta.

# Use the native inference API to send a text message to Meta Llama 3
# 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., Llama 3 8b Instruct.
model_id = "meta.llama3-8b-instruct-v1:0"

# Define the message to send.
user_message = "Describe the purpose of a 'hello world' program in one line."

# Embed the message in Llama 3's prompt format.
prompt = f"""
<|begin_of_text|>
<|start_header_id|>user<|end_header_id|>
{user_message}
<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
"""

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

# 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 "generation" in chunk:
        print(chunk["generation"], end="")