Hello Amazon Bedrock - Amazon Bedrock

Hello Amazon Bedrock

The following code examples show how to get started using Amazon Bedrock.

Go
SDK for Go V2
Note

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

package main import ( "context" "encoding/json" "flag" "fmt" "log" "os" "strings" "github.com/aws/aws-sdk-go-v2/aws" "github.com/aws/aws-sdk-go-v2/config" "github.com/aws/aws-sdk-go-v2/service/bedrockruntime" ) // Each model provider defines their own individual request and response formats. // For the format, ranges, and default values for the different models, refer to: // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html type ClaudeRequest struct { Prompt string `json:"prompt"` MaxTokensToSample int `json:"max_tokens_to_sample"` // Omitting optional request parameters } type ClaudeResponse struct { Completion string `json:"completion"` } // main uses the AWS SDK for Go (v2) to create an Amazon Bedrock Runtime client // and invokes Anthropic Claude 2 inside your account and the chosen region. // This example uses the default settings specified in your shared credentials // and config files. func main() { region := flag.String("region", "us-east-1", "The AWS region") flag.Parse() fmt.Printf("Using AWS region: %s\n", *region) ctx := context.Background() sdkConfig, err := config.LoadDefaultConfig(ctx, config.WithRegion(*region)) if err != nil { fmt.Println("Couldn't load default configuration. Have you set up your AWS account?") fmt.Println(err) return } client := bedrockruntime.NewFromConfig(sdkConfig) modelId := "anthropic.claude-v2" prompt := "Hello, how are you today?" // Anthropic Claude requires you to enclose the prompt as follows: prefix := "Human: " postfix := "\n\nAssistant:" wrappedPrompt := prefix + prompt + postfix request := ClaudeRequest{ Prompt: wrappedPrompt, MaxTokensToSample: 200, } body, err := json.Marshal(request) if err != nil { log.Panicln("Couldn't marshal the request: ", err) } result, err := client.InvokeModel(ctx, &bedrockruntime.InvokeModelInput{ ModelId: aws.String(modelId), ContentType: aws.String("application/json"), Body: body, }) if err != nil { errMsg := err.Error() if strings.Contains(errMsg, "no such host") { fmt.Printf("Error: The Bedrock service is not available in the selected region. Please double-check the service availability for your region at https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/.\n") } else if strings.Contains(errMsg, "Could not resolve the foundation model") { fmt.Printf("Error: Could not resolve the foundation model from model identifier: \"%v\". Please verify that the requested model exists and is accessible within the specified region.\n", modelId) } else { fmt.Printf("Error: Couldn't invoke Anthropic Claude. Here's why: %v\n", err) } os.Exit(1) } var response ClaudeResponse err = json.Unmarshal(result.Body, &response) if err != nil { log.Fatal("failed to unmarshal", err) } fmt.Println("Prompt:\n", prompt) fmt.Println("Response from Anthropic Claude:\n", response.Completion) }
  • For API details, see InvokeModel in AWS SDK for Go 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.

/** * @typedef {Object} Content * @property {string} text * * @typedef {Object} Usage * @property {number} input_tokens * @property {number} output_tokens * * @typedef {Object} ResponseBody * @property {Content[]} content * @property {Usage} usage */ import { fileURLToPath } from "node:url"; import { BedrockRuntimeClient, InvokeModelCommand, } from "@aws-sdk/client-bedrock-runtime"; const AWS_REGION = "us-east-1"; const MODEL_ID = "anthropic.claude-3-haiku-20240307-v1:0"; const PROMPT = "Hi. In a short paragraph, explain what you can do."; const hello = async () => { console.log("=".repeat(35)); console.log("Welcome to the Amazon Bedrock demo!"); console.log("=".repeat(35)); console.log("Model: Anthropic Claude 3 Haiku"); console.log(`Prompt: ${PROMPT}\n`); console.log("Invoking model...\n"); // Create a new Bedrock Runtime client instance. const client = new BedrockRuntimeClient({ region: AWS_REGION }); // Prepare the payload for the model. const payload = { anthropic_version: "bedrock-2023-05-31", max_tokens: 1000, messages: [{ role: "user", content: [{ type: "text", text: PROMPT }] }], }; // Invoke Claude with the payload and wait for the response. const apiResponse = await client.send( new InvokeModelCommand({ contentType: "application/json", body: JSON.stringify(payload), modelId: MODEL_ID, }), ); // Decode and return the response(s) const decodedResponseBody = new TextDecoder().decode(apiResponse.body); /** @type {ResponseBody} */ const responseBody = JSON.parse(decodedResponseBody); const responses = responseBody.content; if (responses.length === 1) { console.log(`Response: ${responses[0].text}`); } else { console.log("Haiku returned multiple responses:"); console.log(responses); } console.log(`\nNumber of input tokens: ${responseBody.usage.input_tokens}`); console.log(`Number of output tokens: ${responseBody.usage.output_tokens}`); }; if (process.argv[1] === fileURLToPath(import.meta.url)) { await hello(); }
  • For API details, see InvokeModel 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 prompt to a model with the InvokeModel operation.

""" Uses the Amazon Bedrock runtime client InvokeModel operation to send a prompt to a model. """ import logging import json import boto3 from botocore.exceptions import ClientError logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) def invoke_model(brt, model_id, prompt): """ Invokes the specified model with the supplied prompt. param brt: A bedrock runtime boto3 client param model_id: The model ID for the model that you want to use. param prompt: The prompt that you want to send to the model. :return: The text response from the model. """ # Format the request payload using the model's native structure. native_request = { "inputText": prompt, "textGenerationConfig": { "maxTokenCount": 512, "temperature": 0.5, "topP": 0.9 } } # Convert the native request to JSON. request = json.dumps(native_request) try: # Invoke the model with the request. response = brt.invoke_model(modelId=model_id, body=request) # Decode the response body. model_response = json.loads(response["body"].read()) # Extract and print the response text. response_text = model_response["results"][0]["outputText"] return response_text except (ClientError, Exception) as e: print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}") raise def main(): """Entry point for the example. Uses the AWS SDK for Python (Boto3) to create an Amazon Bedrock runtime client. Then sends a prompt to a model in the region set in the callers profile and credentials. """ # Create an Amazon Bedrock Runtime client. brt = boto3.client("bedrock-runtime") # Set the model ID, e.g., Amazon Titan Text G1 - Express. model_id = "amazon.titan-text-express-v1" # Define the prompt for the model. prompt = "Describe the purpose of a 'hello world' program in one line." # Send the prompt to the model. response = invoke_model(brt, model_id, prompt) print(f"Response: {response}") logger.info("Done.") if __name__ == "__main__": main()

Send a user message to a model with the Converse operation.

""" Uses the Amazon Bedrock runtime client Converse operation to send a user message to a model. """ import logging import boto3 from botocore.exceptions import ClientError logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) def converse(brt, model_id, user_message): """ Uses the Converse operation to send a user message to the supplied model. param brt: A bedrock runtime boto3 client param model_id: The model ID for the model that you want to use. param user message: The user message that you want to send to the model. :return: The text response from the model. """ # Format the request payload using the model's native structure. conversation = [ { "role": "user", "content": [{"text": user_message}], } ] try: # Send the message to the model, using a basic inference configuration. response = brt.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"] return response_text except (ClientError, Exception) as e: print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}") raise def main(): """Entry point for the example. Uses the AWS SDK for Python (Boto3) to create an Amazon Bedrock runtime client. Then sends a user message to a model in the region set in the callers profile and credentials. """ # Create an Amazon Bedrock Runtime client. brt = boto3.client("bedrock-runtime") # Set the model ID, e.g., Amazon Titan Text G1 - Express. model_id = "amazon.titan-text-express-v1" # Define the message for the model. message = "Describe the purpose of a 'hello world' program in one line." # Send the message to the model. response = converse(brt, model_id, message) print(f"Response: {response}") logger.info("Done.") if __name__ == "__main__": main()
  • For API details, see InvokeModel 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.