AWS 文档 AWS SDK示例 GitHub 存储库中还有更多SDK示例
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使用 Bedrock 的 Convers API e 和响应流在 Amazon Bedrock 上调用 Meta Llama
以下代码示例展示了如何使用 Bedrock 的 Converse 向 Meta Llama 发送短信API并实时处理响应流。
- .NET
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- AWS SDK for .NET
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注意
还有更多相关信息 GitHub。在 AWS 代码示例存储库
中查找完整实例,了解如何进行设置和运行。 使用 Bedrock 的 Converse 向 Meta Llama 发送短信,API并实时处理响应流。
// Use the Converse API to send a text message to Meta Llama // and print the response stream. using System; using System.Collections.Generic; using System.Linq; using Amazon; using Amazon.BedrockRuntime; using Amazon.BedrockRuntime.Model; // Create a Bedrock Runtime client in the AWS Region you want to use. var client = new AmazonBedrockRuntimeClient(RegionEndpoint.USEast1); // Set the model ID, e.g., Llama 3 8b Instruct. var modelId = "meta.llama3-8b-instruct-v1:0"; // Define the user message. var userMessage = "Describe the purpose of a 'hello world' program in one line."; // Create a request with the model ID, the user message, and an inference configuration. var request = new ConverseStreamRequest { ModelId = modelId, Messages = new List<Message> { new Message { Role = ConversationRole.User, Content = new List<ContentBlock> { new ContentBlock { Text = userMessage } } } }, InferenceConfig = new InferenceConfiguration() { MaxTokens = 512, Temperature = 0.5F, TopP = 0.9F } }; try { // Send the request to the Bedrock Runtime and wait for the result. var response = await client.ConverseStreamAsync(request); // Extract and print the streamed response text in real-time. foreach (var chunk in response.Stream.AsEnumerable()) { if (chunk is ContentBlockDeltaEvent) { Console.Write((chunk as ContentBlockDeltaEvent).Delta.Text); } } } catch (AmazonBedrockRuntimeException e) { Console.WriteLine($"ERROR: Can't invoke '{modelId}'. Reason: {e.Message}"); throw; }
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有关API详细信息,请参阅 “AWS SDK for .NET API参考 ConverseStream” 中的。
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- Java
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- SDK适用于 Java 2.x
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注意
还有更多相关信息 GitHub。在 AWS 代码示例存储库
中查找完整实例,了解如何进行设置和运行。 使用 Bedrock 的 Converse 向 Meta Llama 发送短信,API并实时处理响应流。
// Use the Converse API to send a text message to Meta Llama // and print the response stream. import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient; import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock; import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole; import software.amazon.awssdk.services.bedrockruntime.model.ConverseStreamResponseHandler; import software.amazon.awssdk.services.bedrockruntime.model.Message; import java.util.concurrent.ExecutionException; public class ConverseStream { public static void main(String[] args) { // Create a Bedrock Runtime client in the AWS Region you want to use. // Replace the DefaultCredentialsProvider with your preferred credentials provider. var client = BedrockRuntimeAsyncClient.builder() .credentialsProvider(DefaultCredentialsProvider.create()) .region(Region.US_EAST_1) .build(); // Set the model ID, e.g., Llama 3 8b Instruct. var modelId = "meta.llama3-8b-instruct-v1:0"; // Create the input text and embed it in a message object with the user role. var inputText = "Describe the purpose of a 'hello world' program in one line."; var message = Message.builder() .content(ContentBlock.fromText(inputText)) .role(ConversationRole.USER) .build(); // Create a handler to extract and print the response text in real-time. var responseStreamHandler = ConverseStreamResponseHandler.builder() .subscriber(ConverseStreamResponseHandler.Visitor.builder() .onContentBlockDelta(chunk -> { String responseText = chunk.delta().text(); System.out.print(responseText); }).build() ).onError(err -> System.err.printf("Can't invoke '%s': %s", modelId, err.getMessage()) ).build(); try { // Send the message with a basic inference configuration and attach the handler. client.converseStream(request -> request .modelId(modelId) .messages(message) .inferenceConfig(config -> config .maxTokens(512) .temperature(0.5F) .topP(0.9F) ), responseStreamHandler).get(); } catch (ExecutionException | InterruptedException e) { System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage()); } } }
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有关API详细信息,请参阅 “AWS SDK for Java 2.x API参考 ConverseStream” 中的。
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- JavaScript
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- SDK对于 JavaScript (v3)
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注意
还有更多相关信息 GitHub。在 AWS 代码示例存储库
中查找完整实例,了解如何进行设置和运行。 使用 Bedrock 的 Converse 向 Meta Llama 发送短信,API并实时处理响应流。
// Use the Conversation API to send a text message to Meta Llama. import { BedrockRuntimeClient, ConverseStreamCommand, } from "@aws-sdk/client-bedrock-runtime"; // Create a Bedrock Runtime client in the AWS Region you want to use. const client = new BedrockRuntimeClient({ region: "us-east-1" }); // Set the model ID, e.g., Llama 3 8b Instruct. const modelId = "meta.llama3-8b-instruct-v1:0"; // Start a conversation with the user message. const userMessage = "Describe the purpose of a 'hello world' program in one line."; const conversation = [ { role: "user", content: [{ text: userMessage }], }, ]; // Create a command with the model ID, the message, and a basic configuration. const command = new ConverseStreamCommand({ modelId, messages: conversation, inferenceConfig: { maxTokens: 512, temperature: 0.5, topP: 0.9 }, }); try { // Send the command to the model and wait for the response const response = await client.send(command); // Extract and print the streamed response text in real-time. for await (const item of response.stream) { if (item.contentBlockDelta) { process.stdout.write(item.contentBlockDelta.delta?.text); } } } catch (err) { console.log(`ERROR: Can't invoke '${modelId}'. Reason: ${err}`); process.exit(1); }
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有关API详细信息,请参阅 “AWS SDK for JavaScript API参考 ConverseStream” 中的。
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- Python
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- SDK适用于 Python (Boto3)
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注意
还有更多相关信息 GitHub。在 AWS 代码示例存储库
中查找完整实例,了解如何进行设置和运行。 使用 Bedrock 的 Converse 向 Meta Llama 发送短信,API并实时处理响应流。
# Use the Conversation API to send a text message to Meta Llama # and print the response stream. import boto3 from botocore.exceptions import ClientError # Create a Bedrock Runtime client in the AWS Region you want to use. client = boto3.client("bedrock-runtime", region_name="us-east-1") # Set the model ID, e.g., Llama 3 8b Instruct. model_id = "meta.llama3-8b-instruct-v1:0" # Start a conversation with the user message. user_message = "Describe the purpose of a 'hello world' program in one line." conversation = [ { "role": "user", "content": [{"text": user_message}], } ] try: # Send the message to the model, using a basic inference configuration. streaming_response = client.converse_stream( modelId=model_id, messages=conversation, inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9}, ) # Extract and print the streamed response text in real-time. for chunk in streaming_response["stream"]: if "contentBlockDelta" in chunk: text = chunk["contentBlockDelta"]["delta"]["text"] print(text, end="") except (ClientError, Exception) as e: print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}") exit(1)
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有关API详细信息,请参阅ConverseStream中的 AWS SDKPython (Boto3) API 参考。
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