使用适用于 Swift 的软件开发工具包的亚马逊 Bedrock

使用 Bedrock 的 Converse API 向 Amazon Nova 发送文本消息。

// An example demonstrating how to use the Conversation API to send 
// a text message to Amazon Nova.

import AWSBedrockRuntime

func converse(_ textPrompt: String) async throws -> String {

    // Create a Bedrock Runtime client in the AWS Region you want to use.
    let config =
        try await BedrockRuntimeClient.BedrockRuntimeClientConfiguration(
            region: "us-east-1"
        )
    let client = BedrockRuntimeClient(config: config)

    // Set the model ID.
    let modelId = "amazon.nova-micro-v1:0"

    // Start a conversation with the user message.
    let message = BedrockRuntimeClientTypes.Message(
        content: [.text(textPrompt)],
        role: .user
    )

    // Optionally use inference parameters
    let inferenceConfig =
        BedrockRuntimeClientTypes.InferenceConfiguration(
            maxTokens: 512,
            stopSequences: ["END"],
            temperature: 0.5,
            topp: 0.9
        )

    // Create the ConverseInput to send to the model
    let input = ConverseInput(
        inferenceConfig: inferenceConfig, messages: [message], modelId: modelId)

    // Send the ConverseInput to the model
    let response = try await client.converse(input: input)

    // Extract and return the response text.
    if case let .message(msg) = response.output {
        if case let .text(textResponse) = msg.content![0] {
            return textResponse
        } else {
            return "No text response found in message content"
        }
    } else {
        return "No message found in converse output"
    }
}

使用 Bedrock 的 Converse API 向 Amazon Nova 发送文本消息并实时处理回复流。

// An example demonstrating how to use the Conversation API to send a text message
// to Amazon Nova and print the response stream

import AWSBedrockRuntime

func printConverseStream(_ textPrompt: String) async throws {

    // Create a Bedrock Runtime client in the AWS Region you want to use.
    let config =
        try await BedrockRuntimeClient.BedrockRuntimeClientConfiguration(
            region: "us-east-1"
        )
    let client = BedrockRuntimeClient(config: config)

    // Set the model ID.
    let modelId = "amazon.nova-lite-v1:0"

    // Start a conversation with the user message.
    let message = BedrockRuntimeClientTypes.Message(
        content: [.text(textPrompt)],
        role: .user
    )

    // Optionally use inference parameters.
    let inferenceConfig =
        BedrockRuntimeClientTypes.InferenceConfiguration(
            maxTokens: 512,
            stopSequences: ["END"],
            temperature: 0.5,
            topp: 0.9
        )

    // Create the ConverseStreamInput to send to the model.
    let input = ConverseStreamInput(
        inferenceConfig: inferenceConfig, messages: [message], modelId: modelId)

    // Send the ConverseStreamInput to the model.
    let response = try await client.converseStream(input: input)

    // Extract the streaming response.
    guard let stream = response.stream else {
        print("No stream available")
        return
    }

    // Extract and print the streamed response text in real-time.
    for try await event in stream {
        switch event {
        case .messagestart(_):
            print("\nNova Lite:")

        case .contentblockdelta(let deltaEvent):
            if case .text(let text) = deltaEvent.delta {
                print(text, terminator: "")
            }

        default:
            break
        }
    }
}

使用 Amazon Nova Reel 根据文本提示生成视频。

// This example demonstrates how to use Amazon Nova Reel to generate a video from a text prompt.
// It shows how to:
// - Set up the Amazon Bedrock runtime client
// - Configure a text-to-video request
// - Submit an asynchronous job for video generation
// - Poll for job completion status
// - Access the generated video from S3

import AWSBedrockRuntime
import Foundation
import Smithy

func startTextToVideoGenerationJob(
    bedrockRuntimeClient: BedrockRuntimeClient, prompt: String, outputS3Uri: String
) async throws -> String? {
    // Specify the model ID for text-to-video generation
    let modelId = "amazon.nova-reel-v1:0"

    // Configure the video generation request with additional parameters
    let modelInputSource: [String: Any] = [
        "taskType": "TEXT_VIDEO",
        "textToVideoParams": [
            "text": "\(prompt)"
        ],
        "videoGenerationConfig": [
            "durationSeconds": 6,
            "fps": 24,
            "dimension": "1280x720",
        ],
    ]

    let modelInput = try Document.make(from: modelInputSource)

    let input = StartAsyncInvokeInput(
        modelId: modelId,
        modelInput: modelInput,
        outputDataConfig: .s3outputdataconfig(
            BedrockRuntimeClientTypes.AsyncInvokeS3OutputDataConfig(
                s3Uri: outputS3Uri
            )
        )
    )

    // Invoke the model asynchronously
    let output = try await bedrockRuntimeClient.startAsyncInvoke(input: input)
    return output.invocationArn
}

func queryJobStatus(
    bedrockRuntimeClient: BedrockRuntimeClient, 
    invocationArn: String?
) async throws -> GetAsyncInvokeOutput {
    try await bedrockRuntimeClient.getAsyncInvoke(
        input: GetAsyncInvokeInput(invocationArn: invocationArn))
}

func main() async throws {
    // Create a Bedrock Runtime client
    let config =
        try await BedrockRuntimeClient.BedrockRuntimeClientConfiguration(
            region: "us-east-1"
        )
    let client = BedrockRuntimeClient(config: config)

    // Specify the S3 location for the output video
    let bucket = "s3://REPLACE-WITH-YOUR-S3-BUCKET-NAM"

    print("Submitting video generation job...")
    let invocationArn = try await startTextToVideoGenerationJob(
        bedrockRuntimeClient: client,
        prompt: "A pomegranate juice in a railway station",
        outputS3Uri: bucket
    )
    print("Job started with invocation ARN: \(String(describing:invocationArn))")

    // Poll for job completion
    var status: BedrockRuntimeClientTypes.AsyncInvokeStatus?
    var isReady = false
    var hasFailed = false

    while !isReady && !hasFailed {
        print("\nPolling job status...")
        status = try await queryJobStatus(
            bedrockRuntimeClient: client, invocationArn: invocationArn
        ).status
        switch status {
        case .completed:
            isReady = true
            print("Video is ready\nCheck S3 bucket: \(bucket)")
        case .failed:
            hasFailed = true
            print("Something went wrong")
        case .inProgress:
            print("Job is in progress...")
            try await Task.sleep(nanoseconds: 15 * 1_000_000_000)  // 15 seconds
        default:
            isReady = true
        }
    }
}

do {
    try await main()
} catch {
    print("An error occurred: \(error)")
}

使用 Bedrock 的 Converse API 向 Anthropic Claude 发送文本消息。

// An example demonstrating how to use the Conversation API to send 
// a text message to Anthropic Claude.

import AWSBedrockRuntime

func converse(_ textPrompt: String) async throws -> String {

    // Create a Bedrock Runtime client in the AWS Region you want to use.
    let config =
        try await BedrockRuntimeClient.BedrockRuntimeClientConfiguration(
            region: "us-east-1"
        )
    let client = BedrockRuntimeClient(config: config)

    // Set the model ID.
    let modelId = "anthropic.claude-3-haiku-20240307-v1:0"

    // Start a conversation with the user message.
    let message = BedrockRuntimeClientTypes.Message(
        content: [.text(textPrompt)],
        role: .user
    )

    // Optionally use inference parameters
    let inferenceConfig =
        BedrockRuntimeClientTypes.InferenceConfiguration(
            maxTokens: 512,
            stopSequences: ["END"],
            temperature: 0.5,
            topp: 0.9
        )

    // Create the ConverseInput to send to the model
    let input = ConverseInput(
        inferenceConfig: inferenceConfig, messages: [message], modelId: modelId)

    // Send the ConverseInput to the model
    let response = try await client.converse(input: input)

    // Extract and return the response text.
    if case let .message(msg) = response.output {
        if case let .text(textResponse) = msg.content![0] {
            return textResponse
        } else {
            return "No text response found in message content"
        }
    } else {
        return "No message found in converse output"
    }
}

使用 Bedrock 的 Converse API 向 Anthropic Claude 发送文本消息并实时处理响应流。

// An example demonstrating how to use the Conversation API to send a text message
// to Anthropic Claude and print the response stream

import AWSBedrockRuntime

func printConverseStream(_ textPrompt: String) async throws {

    // Create a Bedrock Runtime client in the AWS Region you want to use.
    let config =
        try await BedrockRuntimeClient.BedrockRuntimeClientConfiguration(
            region: "us-east-1"
        )
    let client = BedrockRuntimeClient(config: config)

    // Set the model ID.
    let modelId = "anthropic.claude-3-haiku-20240307-v1:0"

    // Start a conversation with the user message.
    let message = BedrockRuntimeClientTypes.Message(
        content: [.text(textPrompt)],
        role: .user
    )

    // Optionally use inference parameters.
    let inferenceConfig =
        BedrockRuntimeClientTypes.InferenceConfiguration(
            maxTokens: 512,
            stopSequences: ["END"],
            temperature: 0.5,
            topp: 0.9
        )

    // Create the ConverseStreamInput to send to the model.
    let input = ConverseStreamInput(
        inferenceConfig: inferenceConfig, messages: [message], modelId: modelId)

    // Send the ConverseStreamInput to the model.
    let response = try await client.converseStream(input: input)

    // Extract the streaming response.
    guard let stream = response.stream else {
        print("No stream available")
        return
    }

    // Extract and print the streamed response text in real-time.
    for try await event in stream {
        switch event {
        case .messagestart(_):
            print("\nAnthropic Claude:")

        case .contentblockdelta(let deltaEvent):
            if case .text(let text) = deltaEvent.delta {
                print(text, terminator: "")
            }

        default:
            break
        }
    }
}

使用 Bedrock 的 Converse API 向 Meta Llama 发送文本消息。

// An example demonstrating how to use the Conversation API to send 
// a text message to Meta Llama.

import AWSBedrockRuntime

func converse(_ textPrompt: String) async throws -> String {

    // Create a Bedrock Runtime client in the AWS Region you want to use.
    let config =
        try await BedrockRuntimeClient.BedrockRuntimeClientConfiguration(
            region: "us-east-1"
        )
    let client = BedrockRuntimeClient(config: config)

    // Set the model ID.
    let modelId = "meta.llama3-8b-instruct-v1:0"

    // Start a conversation with the user message.
    let message = BedrockRuntimeClientTypes.Message(
        content: [.text(textPrompt)],
        role: .user
    )

    // Optionally use inference parameters
    let inferenceConfig =
        BedrockRuntimeClientTypes.InferenceConfiguration(
            maxTokens: 512,
            stopSequences: ["END"],
            temperature: 0.5,
            topp: 0.9
        )

    // Create the ConverseInput to send to the model
    let input = ConverseInput(
        inferenceConfig: inferenceConfig, messages: [message], modelId: modelId)

    // Send the ConverseInput to the model
    let response = try await client.converse(input: input)

    // Extract and return the response text.
    if case let .message(msg) = response.output {
        if case let .text(textResponse) = msg.content![0] {
            return textResponse
        } else {
            return "No text response found in message content"
        }
    } else {
        return "No message found in converse output"
    }
}

使用 Bedrock 的 Converse API 向 Meta Llama 发送文本消息并实时处理响应流。

// An example demonstrating how to use the Conversation API to send a text message
// to Meta Llama and print the response stream.

import AWSBedrockRuntime

func printConverseStream(_ textPrompt: String) async throws {

    // Create a Bedrock Runtime client in the AWS Region you want to use.
    let config =
        try await BedrockRuntimeClient.BedrockRuntimeClientConfiguration(
            region: "us-east-1"
        )
    let client = BedrockRuntimeClient(config: config)

    // Set the model ID.
    let modelId = "meta.llama3-8b-instruct-v1:0"

    // Start a conversation with the user message.
    let message = BedrockRuntimeClientTypes.Message(
        content: [.text(textPrompt)],
        role: .user
    )

    // Optionally use inference parameters.
    let inferenceConfig =
        BedrockRuntimeClientTypes.InferenceConfiguration(
            maxTokens: 512,
            stopSequences: ["END"],
            temperature: 0.5,
            topp: 0.9
        )

    // Create the ConverseStreamInput to send to the model.
    let input = ConverseStreamInput(
        inferenceConfig: inferenceConfig, messages: [message], modelId: modelId)

    // Send the ConverseStreamInput to the model.
    let response = try await client.converseStream(input: input)

    // Extract the streaming response.
    guard let stream = response.stream else {
        print("No stream available")
        return
    }

    // Extract and print the streamed response text in real-time.
    for try await event in stream {
        switch event {
        case .messagestart(_):
            print("\nMeta Llama:")

        case .contentblockdelta(let deltaEvent):
            if case .text(let text) = deltaEvent.delta {
                print(text, terminator: "")
            }

        default:
            break
        }
    }
}