使用 for 的 Amazon 基岩运行时示例 SDK PHP

LLMs在 Amazon Bedrock 上调用多个。

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

使用调用模型API发送短信。

    public function invokeJurassic2($prompt)
    {
        # The different model providers have individual request and response formats.
        # For the format, ranges, and default values for AI21 Labs Jurassic-2, refer to:
        # https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-jurassic2.html

        $completion = "";
        try {
            $modelId = 'ai21.j2-mid-v1';
            $body = [
                'prompt' => $prompt,
                'temperature' => 0.5,
                'maxTokens' => 200,
            ];
            $result = $this->bedrockRuntimeClient->invokeModel([
                'contentType' => 'application/json',
                'body' => json_encode($body),
                'modelId' => $modelId,
            ]);
            $response_body = json_decode($result['body']);
            $completion = $response_body->completions[0]->data->text;
        } catch (Exception $e) {
            echo "Error: ({$e->getCode()}) - {$e->getMessage()}\n";
        }

        return $completion;
    }

使用 Amazon Titan 图像生成器创建图片。

    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;
    }

调用 Anthropic Claude 2 基础模型以生成文本。

    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-v2';
        // Claude requires you to enclose the prompt as follows:
            $prompt = "\n\nHuman: {$prompt}\n\nAssistant:";
            $body = [
                'prompt' => $prompt,
                'max_tokens_to_sample' => 200,
                'temperature' => 0.5,
                'stop_sequences' => ["\n\nHuman:"],
            ];
            $result = $this->bedrockRuntimeClient->invokeModel([
                'contentType' => 'application/json',
                'body' => json_encode($body),
                'modelId' => $modelId,
            ]);
            $response_body = json_decode($result['body']);
            $completion = $response_body->completion;
        } catch (Exception $e) {
            echo "Error: ({$e->getCode()}) - {$e->getMessage()}\n";
        }

        return $completion;
    }

使用稳定扩散创建图像。

    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;
    }