Invoke Meta Llama 3 on Amazon Bedrock using the Invoke Model API
The following code examples show how to send a text message to Meta Llama 3, using the Invoke Model API.
- .NET
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- AWS SDK for .NET
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository
. Use the Invoke Model API to send a text message.
// Use the native inference API to send a text message to Meta Llama 3. 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.USWest2); // Set the model ID, e.g., Llama 3 70b Instruct. var modelId = "meta.llama3-70b-instruct-v1:0"; // Define the prompt for the model. var prompt = "Describe the purpose of a 'hello world' program in one line."; // Embed the prompt in Llama 2's instruction format. var formattedPrompt = $@" <|begin_of_text|><|start_header_id|>user<|end_header_id|> {prompt} <|eot_id|> <|start_header_id|>assistant<|end_header_id|> "; //Format the request payload using the model's native structure. var nativeRequest = JsonSerializer.Serialize(new { prompt = formattedPrompt, max_gen_len = 512, temperature = 0.5 }); // Create a request with the model ID and the model's native request payload. var request = new InvokeModelRequest() { ModelId = modelId, Body = new MemoryStream(System.Text.Encoding.UTF8.GetBytes(nativeRequest)), ContentType = "application/json" }; try { // Send the request to the Bedrock Runtime and wait for the response. var response = await client.InvokeModelAsync(request); // Decode the response body. var modelResponse = await JsonNode.ParseAsync(response.Body); // Extract and print the response text. var responseText = modelResponse["generation"] ?? ""; Console.WriteLine(responseText); } catch (AmazonBedrockRuntimeException e) { Console.WriteLine($"ERROR: Can't invoke '{modelId}'. Reason: {e.Message}"); throw; }
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For API details, see InvokeModel in AWS SDK for .NET API Reference.
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- Java
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- SDK for Java 2.x
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository
. Use the Invoke Model API to send a text message.
// Use the native inference API to send a text message to Meta Llama 3. 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.core.exception.SdkClientException; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient; public class Llama3_InvokeModel { public static String invokeModel() { // 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_WEST_2) .build(); // Set the model ID, e.g., Llama 3 70b Instruct. var modelId = "meta.llama3-70b-instruct-v1:0"; // The InvokeModel 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-meta.html var nativeRequestTemplate = "{ \"prompt\": \"{{instruction}}\" }"; // Define the prompt for the model. var prompt = "Describe the purpose of a 'hello world' program in one line."; // Embed the prompt in Llama 3's instruction format. var instruction = ( "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\\n" + "{{prompt}} <|eot_id|>\\n" + "<|start_header_id|>assistant<|end_header_id|>\\n" ).replace("{{prompt}}", prompt); // Embed the instruction in the the native request payload. var nativeRequest = nativeRequestTemplate.replace("{{instruction}}", instruction); try { // Encode and send the request to the Bedrock Runtime. var response = client.invokeModel(request -> request .body(SdkBytes.fromUtf8String(nativeRequest)) .modelId(modelId) ); // Decode the response body. var responseBody = new JSONObject(response.body().asUtf8String()); // Retrieve the generated text from the model's response. var text = new JSONPointer("/generation").queryFrom(responseBody).toString(); System.out.println(text); return text; } 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) { invokeModel(); } }
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For API details, see InvokeModel in AWS SDK for Java 2.x API Reference.
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- JavaScript
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- SDK for JavaScript (v3)
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository
. Use the Invoke Model API to send a text message.
// Send a prompt to Meta Llama 3 and print the response. import { BedrockRuntimeClient, InvokeModelCommand, } 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 70B Instruct. const modelId = "meta.llama3-70b-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 response = await client.send( new InvokeModelCommand({ contentType: "application/json", body: JSON.stringify(request), modelId, }), ); // Decode the native response body. /** @type {{ generation: string }} */ const nativeResponse = JSON.parse(new TextDecoder().decode(response.body)); // Extract and print the generated text. const responseText = nativeResponse.generation; console.log(responseText); // 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
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For API details, see InvokeModel in AWS SDK for JavaScript API Reference.
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- Python
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- SDK for Python (Boto3)
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Note
There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository
. Use the Invoke Model API to send a text message.
# Use the native inference API to send a text message to Meta Llama 3. import boto3 import json from botocore.exceptions import ClientError # Create a Bedrock Runtime client in the AWS Region of your choice. client = boto3.client("bedrock-runtime", region_name="us-west-2") # Set the model ID, e.g., Llama 3 70b Instruct. model_id = "meta.llama3-70b-instruct-v1:0" # Define the prompt for the model. prompt = "Describe the purpose of a 'hello world' program in one line." # Embed the prompt in Llama 3's instruction format. formatted_prompt = f""" <|begin_of_text|><|start_header_id|>user<|end_header_id|> {prompt} <|eot_id|> <|start_header_id|>assistant<|end_header_id|> """ # Format the request payload using the model's native structure. native_request = { "prompt": formatted_prompt, "max_gen_len": 512, "temperature": 0.5, } # Convert the native request to JSON. request = json.dumps(native_request) try: # Invoke the model with the request. response = client.invoke_model(modelId=model_id, body=request) except (ClientError, Exception) as e: print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}") exit(1) # Decode the response body. model_response = json.loads(response["body"].read()) # Extract and print the response text. response_text = model_response["generation"] print(response_text)
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For API details, see InvokeModel in AWS SDK for Python (Boto3) API Reference.
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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.