Rufen Sie Amazon Nova auf Amazon Bedrock mithilfe der Converse-API von Bedrock mit einem Antwortstream auf

Senden Sie mithilfe der Converse-API von Bedrock eine Textnachricht an Amazon Nova und verarbeiten Sie den Antwortstream in Echtzeit.

// Use the Converse API to send a text message to Amazon Nova
// and print the response stream.

using System;
using System.Collections.Generic;
using System.Linq;
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., Amazon Nova Lite.
var modelId = "amazon.nova-lite-v1:0";

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

// Create a request with the model ID, the user message, and an inference configuration.
var request = new ConverseStreamRequest
{
    ModelId = modelId,
    Messages = new List<Message>
    {
        new Message
        {
            Role = ConversationRole.User,
            Content = new List<ContentBlock> { new ContentBlock { Text = userMessage } }
        }
    },
    InferenceConfig = new InferenceConfiguration()
    {
        MaxTokens = 512,
        Temperature = 0.5F,
        TopP = 0.9F
    }
};

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

    // Extract and print the streamed response text in real-time.
    foreach (var chunk in response.Stream.AsEnumerable())
    {
        if (chunk is ContentBlockDeltaEvent)
        {
            Console.Write((chunk as ContentBlockDeltaEvent).Delta.Text);
        }
    }
}
catch (AmazonBedrockRuntimeException e)
{
    Console.WriteLine($"ERROR: Can't invoke '{modelId}'. Reason: {e.Message}");
    throw;
}

Senden Sie mithilfe der Converse-API von Bedrock eine Textnachricht an Amazon Nova und verarbeiten Sie den Antwortstream in Echtzeit.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.*;

import java.util.concurrent.ExecutionException;

/**
 * This example demonstrates how to use the Amazon Nova foundation models with an
 * asynchronous Amazon Bedrock runtime client to generate streaming text responses.
 * It shows how to:
 * - Set up the Amazon Bedrock runtime client
 * - Create a message
 * - Configure a streaming request
 * - Set up a stream handler to process the response chunks
 * - Process the streaming response
 */
public class ConverseStream {

    public static void converseStream() {

        // Step 1: Create the Amazon Bedrock runtime client
        // The runtime client handles the communication with AI models on Amazon Bedrock
        BedrockRuntimeAsyncClient client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Step 2: Specify which model to use
        // Available Amazon Nova models and their characteristics:
        // - Amazon Nova Micro: Text-only model optimized for lowest latency and cost
        // - Amazon Nova Lite:  Fast, low-cost multimodal model for image, video, and text
        // - Amazon Nova Pro:   Advanced multimodal model balancing accuracy, speed, and cost
        //
        // For the latest available models, see:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
        String modelId = "amazon.nova-lite-v1:0";

        // Step 3: Create the message
        // The message includes the text prompt and specifies that it comes from the user
        var inputText = "Describe the purpose of a 'hello world' program in one paragraph";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        // Step 4: Configure the request
        // Optional parameters to control the model's response:
        // - maxTokens: maximum number of tokens to generate
        // - temperature: randomness (max: 1.0, default: 0.7)
        //   OR
        // - topP: diversity of word choice (max: 1.0, default: 0.9)
        // Note: Use either temperature OR topP, but not both
        ConverseStreamRequest request = ConverseStreamRequest.builder()
                .modelId(modelId)
                .messages(message)
                .inferenceConfig(config -> config
                                .maxTokens(500)     // The maximum response length
                                .temperature(0.5F)  // Using temperature for randomness control
                        //.topP(0.9F)       // Alternative: use topP instead of temperature
                ).build();

        // Step 5: Set up the stream handler
        // The stream handler processes chunks of the response as they arrive
        // - onContentBlockDelta: Processes each text chunk
        // - onError: Handles any errors during streaming
        var streamHandler = ConverseStreamResponseHandler.builder()
                .subscriber(ConverseStreamResponseHandler.Visitor.builder()
                        .onContentBlockDelta(chunk -> {
                            System.out.print(chunk.delta().text());
                            System.out.flush();  // Ensure immediate output of each chunk
                        }).build())
                .onError(err -> System.err.printf("Can't invoke '%s': %s", modelId, err.getMessage()))
                .build();

        // Step 6: Send the streaming request and process the response
        // - Send the request to the model
        // - Attach the handler to process response chunks as they arrive
        // - Handle any errors during streaming
        try {
            client.converseStream(request, streamHandler).get();

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

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

Senden Sie mithilfe der Converse-API von Bedrock eine Textnachricht an Amazon Nova und verarbeiten Sie den Antwortstream in Echtzeit.

// Use the Conversation API to send a text message to Mistral.

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

// Create a Bedrock Runtime client in the AWS Region you want to use.
const client = new BedrockRuntimeClient({ region: "us-east-1" });

// Set the model ID, e.g., Mistral Large.
const modelId = "mistral.mistral-large-2402-v1:0";

// Start a conversation with the user message.
const userMessage =
  "Describe the purpose of a 'hello world' program in one line.";
const conversation = [
  {
    role: "user",
    content: [{ text: userMessage }],
  },
];

// Create a command with the model ID, the message, and a basic configuration.
const command = new ConverseCommand({
  modelId,
  messages: conversation,
  inferenceConfig: { maxTokens: 512, temperature: 0.5, topP: 0.9 },
});

try {
  // Send the command to the model and wait for the response
  const response = await client.send(command);

  // Extract and print the response text.
  const responseText = response.output.message.content[0].text;
  console.log(responseText);
} catch (err) {
  console.log(`ERROR: Can't invoke '${modelId}'. Reason: ${err}`);
  process.exit(1);
}

Senden Sie mithilfe der Converse-API von Bedrock eine Textnachricht an Amazon Nova und verarbeiten Sie den Antwortstream in Echtzeit.

# Use the Conversation API to send a text message to Amazon Nova Text
# and print the response stream.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Amazon Nova Lite.
model_id = "amazon.nova-lite-v1:0"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

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

    # Extract and print the streamed response text in real-time.
    for chunk in streaming_response["stream"]:
        if "contentBlockDelta" in chunk:
            text = chunk["contentBlockDelta"]["delta"]["text"]
            print(text, end="")

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