Invoke multiple foundation models on Amazon Bedrock

The following code examples show how to prepare and send a prompt to a variety of large-language models (LLMs) on 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.

Invoke multiple foundation models on Amazon Bedrock.

// InvokeModelsScenario demonstrates how to use the Amazon Bedrock Runtime client
// to invoke various foundation models for text and image generation
//
// 1. Generate text with Anthropic Claude 2
// 2. Generate text with AI21 Labs Jurassic-2
// 3. Generate text with Meta Llama 2 Chat
// 4. Generate text and asynchronously process the response stream with Anthropic Claude 2
// 5. Generate and image with the Amazon Titan image generation model
// 6. Generate text with Amazon Titan Text G1 Express model
type InvokeModelsScenario struct {
	sdkConfig             aws.Config
	invokeModelWrapper    actions.InvokeModelWrapper
	responseStreamWrapper actions.InvokeModelWithResponseStreamWrapper
	questioner            demotools.IQuestioner
}

// NewInvokeModelsScenario constructs an InvokeModelsScenario instance from a configuration.
// It uses the specified config to get a Bedrock Runtime client and create wrappers for the
// actions used in the scenario.
func NewInvokeModelsScenario(sdkConfig aws.Config, questioner demotools.IQuestioner) InvokeModelsScenario {
	client := bedrockruntime.NewFromConfig(sdkConfig)
	return InvokeModelsScenario{
		sdkConfig:             sdkConfig,
		invokeModelWrapper:    actions.InvokeModelWrapper{BedrockRuntimeClient: client},
		responseStreamWrapper: actions.InvokeModelWithResponseStreamWrapper{BedrockRuntimeClient: client},
		questioner:            questioner,
	}
}

// Runs the interactive scenario.
func (scenario InvokeModelsScenario) Run() {
	defer func() {
		if r := recover(); r != nil {
			log.Printf("Something went wrong with the demo: %v\n", r)
		}
	}()

	log.Println(strings.Repeat("=", 77))
	log.Println("Welcome to the Amazon Bedrock Runtime model invocation demo.")
	log.Println(strings.Repeat("=", 77))

	log.Printf("First, let's invoke a few large-language models using the synchronous client:\n\n")

	text2textPrompt := "In one paragraph, who are you?"

	log.Println(strings.Repeat("-", 77))
	log.Printf("Invoking Claude with prompt: %v\n", text2textPrompt)
	scenario.InvokeClaude(text2textPrompt)

	log.Println(strings.Repeat("-", 77))
	log.Printf("Invoking Jurassic-2 with prompt: %v\n", text2textPrompt)
	scenario.InvokeJurassic2(text2textPrompt)

	log.Println(strings.Repeat("-", 77))
	log.Printf("Invoking Llama2 with prompt: %v\n", text2textPrompt)
	scenario.InvokeLlama2(text2textPrompt)

	log.Println(strings.Repeat("=", 77))
	log.Printf("Now, let's invoke Claude with the asynchronous client and process the response stream:\n\n")

	log.Println(strings.Repeat("-", 77))
	log.Printf("Invoking Claude with prompt: %v\n", text2textPrompt)
	scenario.InvokeWithResponseStream(text2textPrompt)

	log.Println(strings.Repeat("=", 77))
	log.Printf("Now, let's create an image with the Amazon Titan image generation model:\n\n")

	text2ImagePrompt := "stylized picture of a cute old steampunk robot"
	seed := rand.Int63n(2147483648)

	log.Println(strings.Repeat("-", 77))
	log.Printf("Invoking Amazon Titan with prompt: %v\n", text2ImagePrompt)
	scenario.InvokeTitanImage(text2ImagePrompt, seed)

	log.Println(strings.Repeat("-", 77))
	log.Printf("Invoking Titan Text Express with prompt: %v\n", text2textPrompt)
	scenario.InvokeTitanText(text2textPrompt)

	log.Println(strings.Repeat("=", 77))
	log.Println("Thanks for watching!")
	log.Println(strings.Repeat("=", 77))
}

func (scenario InvokeModelsScenario) InvokeClaude(prompt string) {
	completion, err := scenario.invokeModelWrapper.InvokeClaude(prompt)
	if err != nil {
		panic(err)
	}
	log.Printf("\nClaude     : %v\n", strings.TrimSpace(completion))
}

func (scenario InvokeModelsScenario) InvokeJurassic2(prompt string) {
	completion, err := scenario.invokeModelWrapper.InvokeJurassic2(prompt)
	if err != nil {
		panic(err)
	}
	log.Printf("\nJurassic-2 : %v\n", strings.TrimSpace(completion))
}

func (scenario InvokeModelsScenario) InvokeLlama2(prompt string) {
	completion, err := scenario.invokeModelWrapper.InvokeLlama2(prompt)
	if err != nil {
		panic(err)
	}
	log.Printf("\nLlama 2    : %v\n\n", strings.TrimSpace(completion))
}

func (scenario InvokeModelsScenario) InvokeWithResponseStream(prompt string) {
	log.Println("\nClaude with response stream:")
	_, err := scenario.responseStreamWrapper.InvokeModelWithResponseStream(prompt)
	if err != nil {
		panic(err)
	}
	log.Println()
}

func (scenario InvokeModelsScenario) InvokeTitanImage(prompt string, seed int64) {
	base64ImageData, err := scenario.invokeModelWrapper.InvokeTitanImage(prompt, seed)
	if err != nil {
		panic(err)
	}
	imagePath := saveImage(base64ImageData, "amazon.titan-image-generator-v1")
	fmt.Printf("The generated image has been saved to %s\n", imagePath)
}

func (scenario InvokeModelsScenario) InvokeTitanText(prompt string) {
	completion, err := scenario.invokeModelWrapper.InvokeTitanText(prompt)
	if err != nil {
		panic(err)
	}
	log.Printf("\nTitan Text Express    : %v\n\n", strings.TrimSpace(completion))
}

• For API details, see the following topics in AWS SDK for Go API Reference.

  • InvokeModel

  • 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.

// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: Apache-2.0

import { fileURLToPath } from "url";
import {
  Scenario,
  ScenarioAction,
  ScenarioInput,
  ScenarioOutput,
} from "@aws-doc-sdk-examples/lib/scenario/index.js";
import { FoundationModels } from "../config/foundation_models.js";

/**
 * @typedef {Object} ModelConfig
 * @property {Function} module
 * @property {Function} invoker
 * @property {string} modelId
 * @property {string} modelName
 */

const greeting = new ScenarioOutput(
  "greeting",
  "Welcome to the Amazon Bedrock Runtime client demo!",
  { header: true },
);

const selectModel = new ScenarioInput("model", "First, select a model:", {
  type: "select",
  choices: Object.values(FoundationModels).map((model) => ({
    name: model.modelName,
    value: model,
  })),
});

const enterPrompt = new ScenarioInput("prompt", "Now, enter your prompt:", {
  type: "input",
});

const printDetails = new ScenarioOutput(
  "print details",
  /**
   * @param {{ model: ModelConfig, prompt: string }} c
   */
  (c) => console.log(`Invoking ${c.model.modelName} with '${c.prompt}'...`),
  { slow: false },
);

const invokeModel = new ScenarioAction(
  "invoke model",
  /**
   * @param {{ model: ModelConfig, prompt: string, response: string }} c
   */
  async (c) => {
    const modelModule = await c.model.module();
    const invoker = c.model.invoker(modelModule);
    c.response = await invoker(c.prompt, c.model.modelId);
  },
);

const printResponse = new ScenarioOutput(
  "print response",
  /**
   * @param {{ response: string }} c
   */
  (c) => c.response,
  { slow: false },
);

const scenario = new Scenario("Amazon Bedrock Runtime Demo", [
  greeting,
  selectModel,
  enterPrompt,
  printDetails,
  invokeModel,
  printResponse,
]);

if (process.argv[1] === fileURLToPath(import.meta.url)) {
  scenario.run();
}

• For API details, see the following topics in AWS SDK for JavaScript API Reference.

  • InvokeModel

  • InvokeModelWithResponseStream

PHP

SDK for PHP

Note

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

Invoke multiple LLMs on Amazon Bedrock.

namespace BedrockRuntime;

class GettingStartedWithBedrockRuntime
{
    protected BedrockRuntimeService $bedrockRuntimeService;

    public function runExample()
    {
        echo "\n";
        echo "---------------------------------------------------------------------\n";
        echo "Welcome to the Amazon Bedrock Runtime getting started demo using PHP!\n";
        echo "---------------------------------------------------------------------\n";

        $clientArgs = [
            'region' => 'us-east-1',
            'version' => 'latest',
            'profile' => 'default',
        ];

        $bedrockRuntimeService = new BedrockRuntimeService($clientArgs);

        $prompt = 'In one paragraph, who are you?';

        echo "\nPrompt: " . $prompt;

        echo "\n\nAnthropic Claude:";
        echo $bedrockRuntimeService->invokeClaude($prompt);

        echo "\n\nAI21 Labs Jurassic-2: ";
        echo $bedrockRuntimeService->invokeJurassic2($prompt);

        echo "\n\nMeta Llama 2 Chat: ";
        echo $bedrockRuntimeService->invokeLlama2($prompt);

        echo "\n---------------------------------------------------------------------\n";

        $image_prompt = 'stylized picture of a cute old steampunk robot';

        echo "\nImage prompt: " . $image_prompt;

        echo "\n\nStability.ai Stable Diffusion XL:\n";
        $diffusionSeed = rand(0, 4294967295);
        $style_preset = 'photographic';
        $base64 = $bedrockRuntimeService->invokeStableDiffusion($image_prompt, $diffusionSeed, $style_preset);
        $image_path = $this->saveImage($base64, 'stability.stable-diffusion-xl');
        echo "The generated images have been saved to $image_path";

        echo "\n\nAmazon Titan Image Generation:\n";
        $titanSeed = rand(0, 2147483647);
        $base64 = $bedrockRuntimeService->invokeTitanImage($image_prompt, $titanSeed);
        $image_path = $this->saveImage($base64, 'amazon.titan-image-generator-v1');
        echo "The generated images have been saved to $image_path";
    }

    private function saveImage($base64_image_data, $model_id): string
    {
        $output_dir = "output";

        if (!file_exists($output_dir)) {
            mkdir($output_dir);
        }

        $i = 1;
        while (file_exists("$output_dir/$model_id" . '_' . "$i.png")) {
            $i++;
        }

        $image_data = base64_decode($base64_image_data);

        $file_path = "$output_dir/$model_id" . '_' . "$i.png";

        $file = fopen($file_path, 'wb');
        fwrite($file, $image_data);
        fclose($file);

        return $file_path;
    }
}

• For API details, see the following topics in AWS SDK for PHP API Reference.

  • InvokeModel

  • InvokeModelWithResponseStream

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