Invoke Meta Llama 2 on Amazon Bedrock using the Invoke Model API with a response stream - Amazon Bedrock

Invoke Meta Llama 2 on Amazon Bedrock using the Invoke Model API with a response stream

The following code examples show how to send a text message to Meta Llama 2, using the Invoke Model API, and print the response stream.

.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.

Use the Invoke Model API to send a text message and process the response stream in real-time.

// Use the native inference API to send a text message to Meta Llama 2 // 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., Llama 2 Chat 13B. var modelId = "meta.llama2-13b-chat-v1"; // 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 = $"<s>[INST] {prompt} [/INST]"; //Format the request payload using the model's native structure. var nativeRequest = JsonSerializer.Serialize(new { prompt = formattedPrompt, max_gen_len = 512, temperature = 0.5 }); // Create a request with the model ID and the model's native request payload. var request = new InvokeModelWithResponseStreamRequest() { ModelId = modelId, Body = new MemoryStream(System.Text.Encoding.UTF8.GetBytes(nativeRequest)), ContentType = "application/json" }; try { // Send the request to the Bedrock Runtime and wait for the response. var streamingResponse = await client.InvokeModelWithResponseStreamAsync(request); // Extract and print the streamed response text in real-time. foreach (var item in streamingResponse.Body) { var chunk = JsonSerializer.Deserialize<JsonObject>((item as PayloadPart).Bytes); var text = chunk["generation"] ?? ""; Console.Write(text); } } catch (AmazonBedrockRuntimeException e) { Console.WriteLine($"ERROR: Can't invoke '{modelId}'. Reason: {e.Message}"); throw; }
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.

Use the Invoke Model API to send a text message and process the response stream in real-time.

// Use the native inference API to send a text message to Meta Llama 2 // 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.concurrent.ExecutionException; import static software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler.Visitor; public class Llama2_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., Llama 2 Chat 13B. var modelId = "meta.llama2-13b-chat-v1"; // 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-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 2's instruction format. var instruction = "<s>[INST] {{prompt}} [/INST]\\n".replace("{{prompt}}", prompt); // Embed the instruction in the the native request payload. var nativeRequest = nativeRequestTemplate.replace("{{instruction}}", instruction); // 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 -> { // Extract and print the text from the model's native response. var response = new JSONObject(chunk.bytes().asUtf8String()); var text = new JSONPointer("/generation").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 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.

Use the Invoke Model API to send a text message and process the response stream in real-time.

// Send a prompt to Meta Llama 2 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 2 Chat 13B. const modelId = "meta.llama2-13b-chat-v1"; // Define the user message to send. const userMessage = "Describe the purpose of a 'hello world' program in one sentence."; // Embed the message in Llama 2's prompt format. const prompt = `<s>[INST] ${userMessage} [/INST]`; // 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
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.

Use the Invoke Model API to send a text message and process the response stream in real-time.

# Use the native inference API to send a text message to Meta Llama 2 # and print the response stream. 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-east-1") # Set the model ID, e.g., Llama 2 Chat 13B. model_id = "meta.llama2-13b-chat-v1" # Define the prompt for the model. prompt = "Describe the purpose of a 'hello world' program in one line." # Embed the prompt in Llama 2's instruction format. formatted_prompt = f"<s>[INST] {prompt} [/INST]" # 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. 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="") except (ClientError, Exception) as e: print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}") exit(1)

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