使用适用于 JavaScript (v3) 的 SDK 的亚马逊 Bedrock 运行时示例 - Amazon SDK for JavaScript
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Amazon SDK for JavaScript V3 API 参考指南详细描述了 Amazon SDK for JavaScript 版本 3 (V3) 的所有 API 操作。

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使用适用于 JavaScript (v3) 的 SDK 的亚马逊 Bedrock 运行时示例

以下代码示例向您展示了如何使用带有 Amazon Bedrock Runtime 的 Amazon SDK for JavaScript (v3) 来执行操作和实现常见场景。

操作是大型程序的代码摘录,必须在上下文中运行。您可以通过操作了解如何调用单个服务函数,还可以通过函数相关场景和跨服务示例的上下文查看操作。

场景 是展示如何通过在同一服务中调用多个函数来完成特定任务的代码示例。

每个示例都包含一个指向的链接 GitHub,您可以在其中找到有关如何在上下文中设置和运行代码的说明。

开始使用

以下代码示例演示了如何开始使用 Amazon Bedrock。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. // SPDX-License-Identifier: Apache-2.0 /** * @typedef {Object} Content * @property {string} text * * @typedef {Object} Usage * @property {number} input_tokens * @property {number} output_tokens * * @typedef {Object} ResponseBody * @property {Content[]} content * @property {Usage} usage */ import { fileURLToPath } from "url"; import { BedrockRuntimeClient, InvokeModelCommand, } from "@aws-sdk/client-bedrock-runtime"; const AWS_REGION = "us-east-1"; const MODEL_ID = "anthropic.claude-3-haiku-20240307-v1:0"; const PROMPT = "Hi. In a short paragraph, explain what you can do."; const hello = async () => { console.log("=".repeat(35)); console.log("Welcome to the Amazon Bedrock demo!"); console.log("=".repeat(35)); console.log("Model: Anthropic Claude 3 Haiku"); console.log(`Prompt: ${PROMPT}\n`); console.log("Invoking model...\n"); // Create a new Bedrock Runtime client instance. const client = new BedrockRuntimeClient({ region: AWS_REGION }); // Prepare the payload for the model. const payload = { anthropic_version: "bedrock-2023-05-31", max_tokens: 1000, messages: [{ role: "user", content: [{ type: "text", text: PROMPT }] }], }; // Invoke Claude with the payload and wait for the response. const apiResponse = await client.send( new InvokeModelCommand({ contentType: "application/json", body: JSON.stringify(payload), modelId: MODEL_ID, }), ); // Decode and return the response(s) const decodedResponseBody = new TextDecoder().decode(apiResponse.body); /** @type {ResponseBody} */ const responseBody = JSON.parse(decodedResponseBody); const responses = responseBody.content; if (responses.length === 1) { console.log(`Response: ${responses[0].text}`); } else { console.log("Haiku returned multiple responses:"); console.log(responses); } console.log(`\nNumber of input tokens: ${responseBody.usage.input_tokens}`); console.log(`Number of output tokens: ${responseBody.usage.output_tokens}`); }; if (process.argv[1] === fileURLToPath(import.meta.url)) { await hello(); }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考InvokeModel中的。

AI21 Labs 侏罗纪-2

以下代码示例展示了如何使用 Bedrock 的 Converse API 向 AI21 Labs Jurassic-2 发送短信。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Bedrock 的 Converse API 向 AI21 Labs Jurassic-2 发送短信。

// Use the Conversation API to send a text message to AI21 Labs Jurassic-2. import { BedrockRuntimeClient, ConverseCommand, } 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., Jurassic-2 Mid. const modelId = "ai21.j2-mid-v1"; // 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 ConverseCommand({ 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 response text. const responseText = response.output.message.content[0].text; console.log(responseText); } catch (err) { console.log(`ERROR: Can't invoke '${modelId}'. Reason: ${err}`); process.exit(1); }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考中的 Converse

以下代码示例展示了如何使用调用模型 API 向 AI21 Labs Jurassic-2 发送短信。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用调用模型 API 发送短信。

// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. // SPDX-License-Identifier: Apache-2.0 import { fileURLToPath } from "url"; import { FoundationModels } from "../../config/foundation_models.js"; import { BedrockRuntimeClient, InvokeModelCommand, } from "@aws-sdk/client-bedrock-runtime"; /** * @typedef {Object} Data * @property {string} text * * @typedef {Object} Completion * @property {Data} data * * @typedef {Object} ResponseBody * @property {Completion[]} completions */ /** * Invokes an AI21 Labs Jurassic-2 model. * * @param {string} prompt - The input text prompt for the model to complete. * @param {string} [modelId] - The ID of the model to use. Defaults to "ai21.j2-mid-v1". */ export const invokeModel = async (prompt, modelId = "ai21.j2-mid-v1") => { // Create a new Bedrock Runtime client instance. const client = new BedrockRuntimeClient({ region: "us-east-1" }); // Prepare the payload for the model. const payload = { prompt, maxTokens: 500, temperature: 0.5, }; // Invoke the model with the payload and wait for the response. const command = new InvokeModelCommand({ contentType: "application/json", body: JSON.stringify(payload), modelId, }); const apiResponse = await client.send(command); // Decode and return the response(s). const decodedResponseBody = new TextDecoder().decode(apiResponse.body); /** @type {ResponseBody} */ const responseBody = JSON.parse(decodedResponseBody); return responseBody.completions[0].data.text; }; // Invoke the function if this file was run directly. if (process.argv[1] === fileURLToPath(import.meta.url)) { const prompt = 'Complete the following in one sentence: "Once upon a time..."'; const modelId = FoundationModels.JURASSIC2_MID.modelId; console.log(`Prompt: ${prompt}`); console.log(`Model ID: ${modelId}`); try { console.log("-".repeat(53)); const response = await invokeModel(prompt, modelId); console.log(response); } catch (err) { console.log(err); } }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考InvokeModel中的。

亚马逊 Titan 文本

以下代码示例展示了如何使用 Bedrock 的 Converse API 向 Amazon Titan Text 发送短信。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Bedrock 的 Converse API 向 Amazon Titan Text 发送短信。

// Use the Conversation API to send a text message to Amazon Titan Text. import { BedrockRuntimeClient, ConverseCommand, } 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., Titan Text Premier. const modelId = "amazon.titan-text-premier-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 ConverseCommand({ 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 response text. const responseText = response.output.message.content[0].text; console.log(responseText); } catch (err) { console.log(`ERROR: Can't invoke '${modelId}'. Reason: ${err}`); process.exit(1); }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考中的 Converse

以下代码示例展示了如何使用 Bedrock 的 Converse API 向 Amazon Titan Text 发送短信并实时处理响应流。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Bedrock 的 Converse API 向 Amazon Titan Text 发送短信,并实时处理响应流。

// Use the Conversation API to send a text message to Amazon Titan Text. 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., Titan Text Premier. const modelId = "amazon.titan-text-premier-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); }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考ConverseStream中的。

以下代码示例展示了如何使用调用模型 API 向 Amazon Titan Text 发送短信。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用调用模型 API 发送短信。

// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. // SPDX-License-Identifier: Apache-2.0 import { fileURLToPath } from "url"; import { FoundationModels } from "../../config/foundation_models.js"; import { BedrockRuntimeClient, InvokeModelCommand, } from "@aws-sdk/client-bedrock-runtime"; /** * @typedef {Object} ResponseBody * @property {Object[]} results */ /** * Invokes an Amazon Titan Text generation model. * * @param {string} prompt - The input text prompt for the model to complete. * @param {string} [modelId] - The ID of the model to use. Defaults to "amazon.titan-text-express-v1". */ export const invokeModel = async ( prompt, modelId = "amazon.titan-text-express-v1", ) => { // Create a new Bedrock Runtime client instance. const client = new BedrockRuntimeClient({ region: "us-east-1" }); // Prepare the payload for the model. const payload = { inputText: prompt, textGenerationConfig: { maxTokenCount: 4096, stopSequences: [], temperature: 0, topP: 1, }, }; // Invoke the model with the payload and wait for the response. const command = new InvokeModelCommand({ contentType: "application/json", body: JSON.stringify(payload), modelId, }); const apiResponse = await client.send(command); // Decode and return the response. const decodedResponseBody = new TextDecoder().decode(apiResponse.body); /** @type {ResponseBody} */ const responseBody = JSON.parse(decodedResponseBody); return responseBody.results[0].outputText; }; // Invoke the function if this file was run directly. if (process.argv[1] === fileURLToPath(import.meta.url)) { const prompt = 'Complete the following in one sentence: "Once upon a time..."'; const modelId = FoundationModels.TITAN_TEXT_G1_EXPRESS.modelId; console.log(`Prompt: ${prompt}`); console.log(`Model ID: ${modelId}`); try { console.log("-".repeat(53)); const response = await invokeModel(prompt, modelId); console.log(response); } catch (err) { console.log(err); } }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考InvokeModel中的。

Anthropic Claude

以下代码示例展示了如何使用 Bedrock 的 Converse API 向 Anthropic Claude 发送短信。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Bedrock 的 Converse API 向 Anthropic Claude 发送短信。

// Use the Conversation API to send a text message to Anthropic Claude. import { BedrockRuntimeClient, ConverseCommand, } 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., Claude 3 Haiku. const modelId = "anthropic.claude-3-haiku-20240307-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 ConverseCommand({ 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 response text. const responseText = response.output.message.content[0].text; console.log(responseText); } catch (err) { console.log(`ERROR: Can't invoke '${modelId}'. Reason: ${err}`); process.exit(1); }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考中的 Converse

以下代码示例展示了如何使用 Bedrock 的 Converse API 向 Anthropic Claude 发送短信并实时处理响应流。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Bedrock 的 Converse API 向 Anthropic Claude 发送短信并实时处理响应流。

// Use the Conversation API to send a text message to Anthropic Claude. 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., Claude 3 Haiku. const modelId = "anthropic.claude-3-haiku-20240307-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); }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考ConverseStream中的。

以下代码示例展示了如何使用 Invoke Model API 向 Anthropic Claude 发送短信。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用调用模型 API 发送短信。

// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. // SPDX-License-Identifier: Apache-2.0 import { fileURLToPath } from "url"; import { FoundationModels } from "../../config/foundation_models.js"; import { BedrockRuntimeClient, InvokeModelCommand, InvokeModelWithResponseStreamCommand, } from "@aws-sdk/client-bedrock-runtime"; /** * @typedef {Object} ResponseContent * @property {string} text * * @typedef {Object} MessagesResponseBody * @property {ResponseContent[]} content * * @typedef {Object} Delta * @property {string} text * * @typedef {Object} Message * @property {string} role * * @typedef {Object} Chunk * @property {string} type * @property {Delta} delta * @property {Message} message */ /** * Invokes Anthropic Claude 3 using the Messages API. * * To learn more about the Anthropic Messages API, go to: * https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html * * @param {string} prompt - The input text prompt for the model to complete. * @param {string} [modelId] - The ID of the model to use. Defaults to "anthropic.claude-3-haiku-20240307-v1:0". */ export const invokeModel = async ( prompt, modelId = "anthropic.claude-3-haiku-20240307-v1:0", ) => { // Create a new Bedrock Runtime client instance. const client = new BedrockRuntimeClient({ region: "us-east-1" }); // Prepare the payload for the model. const payload = { anthropic_version: "bedrock-2023-05-31", max_tokens: 1000, messages: [ { role: "user", content: [{ type: "text", text: prompt }], }, ], }; // Invoke Claude with the payload and wait for the response. const command = new InvokeModelCommand({ contentType: "application/json", body: JSON.stringify(payload), modelId, }); const apiResponse = await client.send(command); // Decode and return the response(s) const decodedResponseBody = new TextDecoder().decode(apiResponse.body); /** @type {MessagesResponseBody} */ const responseBody = JSON.parse(decodedResponseBody); return responseBody.content[0].text; }; /** * Invokes Anthropic Claude 3 and processes the response stream. * * To learn more about the Anthropic Messages API, go to: * https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html * * @param {string} prompt - The input text prompt for the model to complete. * @param {string} [modelId] - The ID of the model to use. Defaults to "anthropic.claude-3-haiku-20240307-v1:0". */ export const invokeModelWithResponseStream = async ( prompt, modelId = "anthropic.claude-3-haiku-20240307-v1:0", ) => { // Create a new Bedrock Runtime client instance. const client = new BedrockRuntimeClient({ region: "us-east-1" }); // Prepare the payload for the model. const payload = { anthropic_version: "bedrock-2023-05-31", max_tokens: 1000, messages: [ { role: "user", content: [{ type: "text", text: prompt }], }, ], }; // Invoke Claude with the payload and wait for the API to respond. const command = new InvokeModelWithResponseStreamCommand({ contentType: "application/json", body: JSON.stringify(payload), modelId, }); const apiResponse = await client.send(command); let completeMessage = ""; // Decode and process the response stream for await (const item of apiResponse.body) { /** @type Chunk */ const chunk = JSON.parse(new TextDecoder().decode(item.chunk.bytes)); const chunk_type = chunk.type; if (chunk_type === "content_block_delta") { const text = chunk.delta.text; completeMessage = completeMessage + text; process.stdout.write(text); } } // Return the final response return completeMessage; }; // Invoke the function if this file was run directly. if (process.argv[1] === fileURLToPath(import.meta.url)) { const prompt = 'Write a paragraph starting with: "Once upon a time..."'; const modelId = FoundationModels.CLAUDE_3_HAIKU.modelId; console.log(`Prompt: ${prompt}`); console.log(`Model ID: ${modelId}`); try { console.log("-".repeat(53)); const response = await invokeModel(prompt, modelId); console.log("\n" + "-".repeat(53)); console.log("Final structured response:"); console.log(response); } catch (err) { console.log(`\n${err}`); } }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考InvokeModel中的。

以下代码示例展示了如何使用 Invoke Model API 向 Anthropic Claude 模型发送短信并打印响应流。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Invoke Model API 发送短信并实时处理响应流。

// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. // SPDX-License-Identifier: Apache-2.0 import { fileURLToPath } from "url"; import { FoundationModels } from "../../config/foundation_models.js"; import { BedrockRuntimeClient, InvokeModelCommand, InvokeModelWithResponseStreamCommand, } from "@aws-sdk/client-bedrock-runtime"; /** * @typedef {Object} ResponseContent * @property {string} text * * @typedef {Object} MessagesResponseBody * @property {ResponseContent[]} content * * @typedef {Object} Delta * @property {string} text * * @typedef {Object} Message * @property {string} role * * @typedef {Object} Chunk * @property {string} type * @property {Delta} delta * @property {Message} message */ /** * Invokes Anthropic Claude 3 using the Messages API. * * To learn more about the Anthropic Messages API, go to: * https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html * * @param {string} prompt - The input text prompt for the model to complete. * @param {string} [modelId] - The ID of the model to use. Defaults to "anthropic.claude-3-haiku-20240307-v1:0". */ export const invokeModel = async ( prompt, modelId = "anthropic.claude-3-haiku-20240307-v1:0", ) => { // Create a new Bedrock Runtime client instance. const client = new BedrockRuntimeClient({ region: "us-east-1" }); // Prepare the payload for the model. const payload = { anthropic_version: "bedrock-2023-05-31", max_tokens: 1000, messages: [ { role: "user", content: [{ type: "text", text: prompt }], }, ], }; // Invoke Claude with the payload and wait for the response. const command = new InvokeModelCommand({ contentType: "application/json", body: JSON.stringify(payload), modelId, }); const apiResponse = await client.send(command); // Decode and return the response(s) const decodedResponseBody = new TextDecoder().decode(apiResponse.body); /** @type {MessagesResponseBody} */ const responseBody = JSON.parse(decodedResponseBody); return responseBody.content[0].text; }; /** * Invokes Anthropic Claude 3 and processes the response stream. * * To learn more about the Anthropic Messages API, go to: * https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html * * @param {string} prompt - The input text prompt for the model to complete. * @param {string} [modelId] - The ID of the model to use. Defaults to "anthropic.claude-3-haiku-20240307-v1:0". */ export const invokeModelWithResponseStream = async ( prompt, modelId = "anthropic.claude-3-haiku-20240307-v1:0", ) => { // Create a new Bedrock Runtime client instance. const client = new BedrockRuntimeClient({ region: "us-east-1" }); // Prepare the payload for the model. const payload = { anthropic_version: "bedrock-2023-05-31", max_tokens: 1000, messages: [ { role: "user", content: [{ type: "text", text: prompt }], }, ], }; // Invoke Claude with the payload and wait for the API to respond. const command = new InvokeModelWithResponseStreamCommand({ contentType: "application/json", body: JSON.stringify(payload), modelId, }); const apiResponse = await client.send(command); let completeMessage = ""; // Decode and process the response stream for await (const item of apiResponse.body) { /** @type Chunk */ const chunk = JSON.parse(new TextDecoder().decode(item.chunk.bytes)); const chunk_type = chunk.type; if (chunk_type === "content_block_delta") { const text = chunk.delta.text; completeMessage = completeMessage + text; process.stdout.write(text); } } // Return the final response return completeMessage; }; // Invoke the function if this file was run directly. if (process.argv[1] === fileURLToPath(import.meta.url)) { const prompt = 'Write a paragraph starting with: "Once upon a time..."'; const modelId = FoundationModels.CLAUDE_3_HAIKU.modelId; console.log(`Prompt: ${prompt}`); console.log(`Model ID: ${modelId}`); try { console.log("-".repeat(53)); const response = await invokeModel(prompt, modelId); console.log("\n" + "-".repeat(53)); console.log("Final structured response:"); console.log(response); } catch (err) { console.log(`\n${err}`); } }

Cohere Command

以下代码示例展示了如何使用 Bedrock 的 Converse API 向 Cohere Command 发送短信。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Bedrock 的 Converse API 向 Cohere Command 发送短信。

// Use the Conversation API to send a text message to Cohere Command. import { BedrockRuntimeClient, ConverseCommand, } 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., Command R. const modelId = "cohere.command-r-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 ConverseCommand({ 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 response text. const responseText = response.output.message.content[0].text; console.log(responseText); } catch (err) { console.log(`ERROR: Can't invoke '${modelId}'. Reason: ${err}`); process.exit(1); }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考中的 Converse

以下代码示例展示了如何使用 Bedrock 的 Converse API 向 Cohere Command 发送短信并实时处理响应流。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Bedrock 的 Converse API 向 Cohere Command 发送短信并实时处理响应流。

// Use the Conversation API to send a text message to Cohere Command. 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., Command R. const modelId = "cohere.command-r-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); }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考ConverseStream中的。

Meta Llama

以下代码示例展示了如何使用 Bedrock 的 Converse API 向 Meta Llama 发送短信。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Bedrock 的 Converse API 向 Meta Llama 发送短信。

// Use the Conversation API to send a text message to Meta Llama. import { BedrockRuntimeClient, ConverseCommand, } 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 ConverseCommand({ 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 response text. const responseText = response.output.message.content[0].text; console.log(responseText); } catch (err) { console.log(`ERROR: Can't invoke '${modelId}'. Reason: ${err}`); process.exit(1); }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考中的 Converse

以下代码示例展示了如何使用 Bedrock 的 Converse API 向 Meta Llama 发送短信并实时处理响应流。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Bedrock 的 Converse API 向 Meta Llama 发送短信并实时处理响应流。

// 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); }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考ConverseStream中的。

以下代码示例展示了如何使用 Invoke Model API 向 Meta Llama 2 发送短信。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用调用模型 API 发送短信。

// Send a prompt to Meta Llama 2 and print the response. import { BedrockRuntimeClient, InvokeModelCommand, } from "@aws-sdk/client-bedrock-runtime"; // Create a Bedrock Runtime client in the AWS Region of your choice. const client = new BedrockRuntimeClient({ region: "us-west-2" }); // Set the model ID, e.g., Llama 2 Chat 13B. const modelId = "meta.llama2-13b-chat-v1"; // Define the user message to send. const userMessage = "Describe the purpose of a 'hello world' program in one sentence."; // Embed the message in Llama 2's prompt format. const prompt = `<s>[INST] ${userMessage} [/INST]`; // Format the request payload using the model's native structure. const request = { prompt, // Optional inference parameters: max_gen_len: 512, temperature: 0.5, top_p: 0.9, }; // Encode and send the request. const response = await client.send( new InvokeModelCommand({ contentType: "application/json", body: JSON.stringify(request), modelId, }), ); // Decode the native response body. /** @type {{ generation: string }} */ const nativeResponse = JSON.parse(new TextDecoder().decode(response.body)); // Extract and print the generated text. const responseText = nativeResponse.generation; console.log(responseText); // Learn more about the Llama 2 prompt format at: // https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-2
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考InvokeModel中的。

以下代码示例展示了如何使用 Invoke Model API 向 Meta Llama 3 发送短信。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用调用模型 API 发送短信。

// Send a prompt to Meta Llama 3 and print the response. import { BedrockRuntimeClient, InvokeModelCommand, } from "@aws-sdk/client-bedrock-runtime"; // Create a Bedrock Runtime client in the AWS Region of your choice. const client = new BedrockRuntimeClient({ region: "us-west-2" }); // Set the model ID, e.g., Llama 3 8B Instruct. const modelId = "meta.llama3-8b-instruct-v1:0"; // Define the user message to send. const userMessage = "Describe the purpose of a 'hello world' program in one sentence."; // Embed the message in Llama 3's prompt format. const prompt = ` <|begin_of_text|> <|start_header_id|>user<|end_header_id|> ${userMessage} <|eot_id|> <|start_header_id|>assistant<|end_header_id|> `; // Format the request payload using the model's native structure. const request = { prompt, // Optional inference parameters: max_gen_len: 512, temperature: 0.5, top_p: 0.9, }; // Encode and send the request. const response = await client.send( new InvokeModelCommand({ contentType: "application/json", body: JSON.stringify(request), modelId, }), ); // Decode the native response body. /** @type {{ generation: string }} */ const nativeResponse = JSON.parse(new TextDecoder().decode(response.body)); // Extract and print the generated text. const responseText = nativeResponse.generation; console.log(responseText); // Learn more about the Llama 3 prompt format at: // https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考InvokeModel中的。

以下代码示例展示了如何使用 Invoke Model API 向 Meta Llama 2 发送短信并打印响应流。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Invoke Model API 发送短信并实时处理响应流。

// Send a prompt to Meta Llama 2 and print the response stream in real-time. import { BedrockRuntimeClient, InvokeModelWithResponseStreamCommand, } from "@aws-sdk/client-bedrock-runtime"; // Create a Bedrock Runtime client in the AWS Region of your choice. const client = new BedrockRuntimeClient({ region: "us-west-2" }); // Set the model ID, e.g., Llama 2 Chat 13B. const modelId = "meta.llama2-13b-chat-v1"; // Define the user message to send. const userMessage = "Describe the purpose of a 'hello world' program in one sentence."; // Embed the message in Llama 2's prompt format. const prompt = `<s>[INST] ${userMessage} [/INST]`; // Format the request payload using the model's native structure. const request = { prompt, // Optional inference parameters: max_gen_len: 512, temperature: 0.5, top_p: 0.9, }; // Encode and send the request. const responseStream = await client.send( new InvokeModelWithResponseStreamCommand({ contentType: "application/json", body: JSON.stringify(request), modelId, }), ); // Extract and print the response stream in real-time. for await (const event of responseStream.body) { /** @type {{ generation: string }} */ const chunk = JSON.parse(new TextDecoder().decode(event.chunk.bytes)); if (chunk.generation) { process.stdout.write(chunk.generation); } } // Learn more about the Llama 3 prompt format at: // https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3

以下代码示例展示了如何使用 Invoke Model API 向 Meta Llama 3 发送短信并打印响应流。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Invoke Model API 发送短信并实时处理响应流。

// Send a prompt to Meta Llama 3 and print the response stream in real-time. import { BedrockRuntimeClient, InvokeModelWithResponseStreamCommand, } from "@aws-sdk/client-bedrock-runtime"; // Create a Bedrock Runtime client in the AWS Region of your choice. const client = new BedrockRuntimeClient({ region: "us-west-2" }); // Set the model ID, e.g., Llama 3 8B Instruct. const modelId = "meta.llama3-8b-instruct-v1:0"; // Define the user message to send. const userMessage = "Describe the purpose of a 'hello world' program in one sentence."; // Embed the message in Llama 3's prompt format. const prompt = ` <|begin_of_text|> <|start_header_id|>user<|end_header_id|> ${userMessage} <|eot_id|> <|start_header_id|>assistant<|end_header_id|> `; // Format the request payload using the model's native structure. const request = { prompt, // Optional inference parameters: max_gen_len: 512, temperature: 0.5, top_p: 0.9, }; // Encode and send the request. const responseStream = await client.send( new InvokeModelWithResponseStreamCommand({ contentType: "application/json", body: JSON.stringify(request), modelId, }), ); // Extract and print the response stream in real-time. for await (const event of responseStream.body) { /** @type {{ generation: string }} */ const chunk = JSON.parse(new TextDecoder().decode(event.chunk.bytes)); if (chunk.generation) { process.stdout.write(chunk.generation); } } // Learn more about the Llama 3 prompt format at: // https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3

Mistral AI

以下代码示例展示了如何使用 Bedrock 的 Converse API 向 Mistral 发送短信。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Bedrock 的 Converse API 向 Mistral 发送短信。

// Use the Conversation API to send a text message to Mistral. import { BedrockRuntimeClient, ConverseCommand, } 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., Mistral Large. const modelId = "mistral.mistral-large-2402-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 ConverseCommand({ 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 response text. const responseText = response.output.message.content[0].text; console.log(responseText); } catch (err) { console.log(`ERROR: Can't invoke '${modelId}'. Reason: ${err}`); process.exit(1); }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考中的 Converse

以下代码示例展示了如何使用 Bedrock 的 Converse API 向 Mistral 发送短信并实时处理响应流。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用 Bedrock 的 Converse API 向 Mistral 发送短信并实时处理响应流。

// Use the Conversation API to send a text message to Mistral. 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., Mistral Large. const modelId = "mistral.mistral-large-2402-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); }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考ConverseStream中的。

以下代码示例展示了如何使用 Invoke Model API 向 Mistral 模型发送短信。

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

使用调用模型 API 发送短信。

// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. // SPDX-License-Identifier: Apache-2.0 import { fileURLToPath } from "url"; import { FoundationModels } from "../../config/foundation_models.js"; import { BedrockRuntimeClient, InvokeModelCommand, } from "@aws-sdk/client-bedrock-runtime"; /** * @typedef {Object} Output * @property {string} text * * @typedef {Object} ResponseBody * @property {Output[]} outputs */ /** * Invokes a Mistral 7B Instruct model. * * @param {string} prompt - The input text prompt for the model to complete. * @param {string} [modelId] - The ID of the model to use. Defaults to "mistral.mistral-7b-instruct-v0:2". */ export const invokeModel = async ( prompt, modelId = "mistral.mistral-7b-instruct-v0:2", ) => { // Create a new Bedrock Runtime client instance. const client = new BedrockRuntimeClient({ region: "us-east-1" }); // Mistral instruct models provide optimal results when embedding // the prompt into the following template: const instruction = `<s>[INST] ${prompt} [/INST]`; // Prepare the payload. const payload = { prompt: instruction, max_tokens: 500, temperature: 0.5, }; // Invoke the model with the payload and wait for the response. const command = new InvokeModelCommand({ contentType: "application/json", body: JSON.stringify(payload), modelId, }); const apiResponse = await client.send(command); // Decode and return the response. const decodedResponseBody = new TextDecoder().decode(apiResponse.body); /** @type {ResponseBody} */ const responseBody = JSON.parse(decodedResponseBody); return responseBody.outputs[0].text; }; // Invoke the function if this file was run directly. if (process.argv[1] === fileURLToPath(import.meta.url)) { const prompt = 'Complete the following in one sentence: "Once upon a time..."'; const modelId = FoundationModels.MISTRAL_7B.modelId; console.log(`Prompt: ${prompt}`); console.log(`Model ID: ${modelId}`); try { console.log("-".repeat(53)); const response = await invokeModel(prompt, modelId); console.log(response); } catch (err) { console.log(err); } }
  • 有关 API 的详细信息,请参阅 Amazon SDK for JavaScript API 参考InvokeModel中的。

场景

以下代码示例展示了如何在 Amazon Bedrock 上准备和向各种大型语言模型 (LLM) 发送提示

适用于 JavaScript (v3) 的软件开发工具包
注意

还有更多相关信息 GitHub。在 Amazon 代码示例存储库中查找完整示例,了解如何进行设置和运行。

// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. // SPDX-License-Identifier: Apache-2.0 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(); }