Beispiele für Amazon Bedrock Runtime unter Verwendung von SDK für PHP

Rufen Sie mehrere LLMs in Amazon Bedrock auf.

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";
        $bedrockRuntimeService = new BedrockRuntimeService();
        $prompt = 'In one paragraph, who are you?';
        echo "\nPrompt: " . $prompt;
        echo "\n\nAnthropic Claude:\n";
        echo $bedrockRuntimeService->invokeClaude($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 image has 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 image has 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;
    }
}

Senden Sie mithilfe der Converse-API von Bedrock eine Textnachricht an Amazon Nova.

// Use the Conversation API to send a text message to Amazon Nova.

use Aws\BedrockRuntime\BedrockRuntimeClient;
use Aws\Exception\AwsException;
use RuntimeException;

class Converse
{
    public function converse(): string
    {
        // Create a Bedrock Runtime client in the AWS Region you want to use.
        $client = new BedrockRuntimeClient([
            'region' => 'us-east-1',
            'profile' => 'default'
        ]);

        // Set the model ID, e.g., Amazon Nova Lite.
        $modelId = 'amazon.nova-lite-v1:0';

        // Start a conversation with the user message.
        $userMessage = "Describe the purpose of a 'hello world' program in one line.";
        $conversation = [
            [
                "role" => "user",
                "content" => [["text" => $userMessage]]
            ]
        ];

        try {
            // Send the message to the model, using a basic inference configuration.
            $response = $client->converse([
                'modelId' => $modelId,
                'messages' => $conversation,
                'inferenceConfig' => [
                    'maxTokens' => 512,
                    'temperature' => 0.5
                ]
            ]);

            // Extract and return the response text.
            $responseText = $response['output']['message']['content'][0]['text'];
            return $responseText;
        } catch (AwsException $e) {
            echo "ERROR: Can't invoke {$modelId}. Reason: {$e->getAwsErrorMessage()}";
            throw new RuntimeException("Failed to invoke model: " . $e->getAwsErrorMessage(), 0, $e);
        }
    }
}

$demo = new Converse();
echo $demo->converse();

Erstellen Sie ein Bild mit dem Amazon Titan Image Generator.

    public function invokeTitanImage(string $prompt, int $seed)
    {
        // The different model providers have individual request and response formats.
        // For the format, ranges, and default values for Titan Image models refer to:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-image.html

        $base64_image_data = "";
        try {
            $modelId = 'amazon.titan-image-generator-v1';
            $request = json_encode([
                'taskType' => 'TEXT_IMAGE',
                'textToImageParams' => [
                    'text' => $prompt
                ],
                'imageGenerationConfig' => [
                    'numberOfImages' => 1,
                    'quality' => 'standard',
                    'cfgScale' => 8.0,
                    'height' => 512,
                    'width' => 512,
                    'seed' => $seed
                ]
            ]);
            $result = $this->bedrockRuntimeClient->invokeModel([
                'contentType' => 'application/json',
                'body' => $request,
                'modelId' => $modelId,
            ]);
            $response_body = json_decode($result['body']);
            $base64_image_data = $response_body->images[0];
        } catch (Exception $e) {
            echo "Error: ({$e->getCode()}) - {$e->getMessage()}\n";
        }

        return $base64_image_data;
    }

Rufen Sie das Basismodell von Anthropic Claude 2 auf, um Text zu generieren.

    public function invokeClaude($prompt)
    {
        // The different model providers have individual request and response formats.
        // For the format, ranges, and default values for Anthropic Claude, refer to:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-claude.html

        $completion = "";
        try {
            $modelId = 'anthropic.claude-3-haiku-20240307-v1:0';
        // Claude requires you to enclose the prompt as follows:
            $body = [
                'anthropic_version' => 'bedrock-2023-05-31',
                'max_tokens' => 512,
                'temperature' => 0.5,
                'messages' => [[
                    'role' => 'user',
                    'content' => $prompt
                ]]
            ];
            $result = $this->bedrockRuntimeClient->invokeModel([
                'contentType' => 'application/json',
                'body' => json_encode($body),
                'modelId' => $modelId,
            ]);
            $response_body = json_decode($result['body']);
            $completion = $response_body->content[0]->text;
        } catch (Exception $e) {
            echo "Error: ({$e->getCode()}) - {$e->getMessage()}\n";
        }

        return $completion;
    }

Erstellen Sie ein Bild mit Stable Diffusion.

    public function invokeStableDiffusion(string $prompt, int $seed, string $style_preset)
    {
        // The different model providers have individual request and response formats.
        // For the format, ranges, and available style_presets of Stable Diffusion models refer to:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-stability-diffusion.html

        $base64_image_data = "";
        try {
            $modelId = 'stability.stable-diffusion-xl-v1';
            $body = [
                'text_prompts' => [
                    ['text' => $prompt]
                ],
                'seed' => $seed,
                'cfg_scale' => 10,
                'steps' => 30
            ];
            if ($style_preset) {
                $body['style_preset'] = $style_preset;
            }

            $result = $this->bedrockRuntimeClient->invokeModel([
                'contentType' => 'application/json',
                'body' => json_encode($body),
                'modelId' => $modelId,
            ]);
            $response_body = json_decode($result['body']);
            $base64_image_data = $response_body->artifacts[0]->base64;
        } catch (Exception $e) {
            echo "Error: ({$e->getCode()}) - {$e->getMessage()}\n";
        }

        return $base64_image_data;
    }