在 Amazon 基岩上調用多個基礎模型

在 Amazon 基岩上調用多個基礎模型。

// 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))
}

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();
}

LLMs在 Amazon 基岩上調用多個。

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;
    }
}