Invoke Meta Llama on Amazon Bedrock using Bedrock's Converse API - Amazon Bedrock

Invoke Meta Llama on Amazon Bedrock using Bedrock's Converse API

The following code examples show how to send a text message to Meta Llama, using Bedrock's Converse API.

.NET
AWS SDK for .NET
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository.

Send a text message to Meta Llama, using Bedrock's Converse API.

// Use the Converse API to send a text message to Meta Llama. using System; using System.Collections.Generic; 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., Llama 3 8b Instruct. var modelId = "meta.llama3-8b-instruct-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 ConverseRequest { 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.ConverseAsync(request); // Extract and print the response text. string responseText = response?.Output?.Message?.Content?[0]?.Text ?? ""; Console.WriteLine(responseText); } catch (AmazonBedrockRuntimeException e) { Console.WriteLine($"ERROR: Can't invoke '{modelId}'. Reason: {e.Message}"); throw; }
  • For API details, see Converse in AWS SDK for .NET API Reference.

Java
SDK for Java 2.x
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository.

Send a text message to Meta Llama, using Bedrock's Converse API.

// Use the Converse API to send a text message to Meta Llama. import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider; import software.amazon.awssdk.core.exception.SdkClientException; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient; import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock; import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole; import software.amazon.awssdk.services.bedrockruntime.model.ConverseResponse; import software.amazon.awssdk.services.bedrockruntime.model.Message; public class Converse { public static String converse() { // 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., Llama 3 8b Instruct. var modelId = "meta.llama3-8b-instruct-v1:0"; // Create the input text and embed it in a message object with the user role. var inputText = "Describe the purpose of a 'hello world' program in one line."; var message = Message.builder() .content(ContentBlock.fromText(inputText)) .role(ConversationRole.USER) .build(); try { // Send the message with a basic inference configuration. ConverseResponse response = client.converse(request -> request .modelId(modelId) .messages(message) .inferenceConfig(config -> config .maxTokens(512) .temperature(0.5F) .topP(0.9F))); // Retrieve the generated text from Bedrock's response object. var responseText = response.output().message().content().get(0).text(); System.out.println(responseText); return responseText; } 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) { converse(); } }

Send a text message to Meta Llama, using Bedrock's Converse API with the async Java client.

// Use the Converse API to send a text message to Meta Llama // with the async Java client. 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.ContentBlock; import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole; import software.amazon.awssdk.services.bedrockruntime.model.Message; import java.util.concurrent.CompletableFuture; import java.util.concurrent.ExecutionException; public class ConverseAsync { public static String converseAsync() { // 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., Llama 3 8b Instruct. var modelId = "meta.llama3-8b-instruct-v1:0"; // Create the input text and embed it in a message object with the user role. var inputText = "Describe the purpose of a 'hello world' program in one line."; var message = Message.builder() .content(ContentBlock.fromText(inputText)) .role(ConversationRole.USER) .build(); // Send the message with a basic inference configuration. var request = client.converse(params -> params .modelId(modelId) .messages(message) .inferenceConfig(config -> config .maxTokens(512) .temperature(0.5F) .topP(0.9F)) ); // Prepare a future object to handle the asynchronous response. CompletableFuture<String> future = new CompletableFuture<>(); // Handle the response or error using the future object. request.whenComplete((response, error) -> { if (error == null) { // Extract the generated text from Bedrock's response object. String responseText = response.output().message().content().get(0).text(); future.complete(responseText); } else { future.completeExceptionally(error); } }); try { // Wait for the future object to complete and retrieve the generated text. String responseText = future.get(); System.out.println(responseText); return responseText; } catch (ExecutionException | InterruptedException e) { System.err.printf("Can't invoke '%s': %s", modelId, e.getMessage()); throw new RuntimeException(e); } } public static void main(String[] args) { converseAsync(); } }
  • For API details, see Converse in AWS SDK for Java 2.x API Reference.

JavaScript
SDK for JavaScript (v3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository.

Send a text message to Meta Llama, using Bedrock's Converse API.

// Use the Conversation API to send a text message to Meta Llama. 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., Llama 3 8b Instruct. const modelId = "meta.llama3-8b-instruct-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); }
  • For API details, see Converse in AWS SDK for JavaScript API Reference.

Python
SDK for Python (Boto3)
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository.

Send a text message to Meta Llama, using Bedrock's Converse API.

# Use the Conversation API to send a text message to Meta Llama. 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., Llama 3 8b Instruct. model_id = "meta.llama3-8b-instruct-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. response = client.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"] print(response_text) except (ClientError, Exception) as e: print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}") exit(1)
  • For API details, see Converse in AWS SDK for Python (Boto3) API Reference.

For a complete list of AWS SDK developer guides and code examples, see Using Amazon Bedrock with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions.