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Add JavaScript examples for Amazon Nova and Amazon Nova Canvas
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‎.doc_gen/metadata/bedrock-runtime_metadata.yaml

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@@ -69,6 +69,23 @@ bedrock-runtime_Converse_Ai21LabsJurassic2:
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services:
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bedrock-runtime: {Converse}
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bedrock-runtime_Converse_AmazonNovaText:
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title: Invoke Amazon Nova on &BR; using Bedrock's Converse API
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title_abbrev: "Converse"
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synopsis: send a text message to Amazon Nova, using Bedrock's Converse API.
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category: Amazon Nova
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languages:
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JavaScript:
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versions:
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- sdk_version: 3
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github: javascriptv3/example_code/bedrock-runtime
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excerpts:
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- description: Send a text message to Amazon Nova, using Bedrock's Converse API.
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snippet_tags:
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- javascript.v3.bedrock-runtime.Converse_AmazonTitanText
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services:
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bedrock-runtime: {Converse}
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bedrock-runtime_Converse_AmazonTitanText:
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title: Invoke Amazon Titan Text on &BR; using Bedrock's Converse API
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title_abbrev: "Converse"
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bedrock-runtime: {Converse}
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# Converse Stream
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bedrock-runtime_ConverseStream_AmazonNovaText:
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title: Invoke Amazon Nova on &BR; using Bedrock's Converse API with a response stream
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title_abbrev: "ConverseStream"
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synopsis: send a text message to Amazon Nova, using Bedrock's Converse API and process the response stream in real-time.
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category: Amazon Nova
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languages:
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JavaScript:
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versions:
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- sdk_version: 3
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github: javascriptv3/example_code/bedrock-runtime
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excerpts:
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- description: Send a text message to Amazon Nova using Bedrock's Converse API and process the response stream in real-time.
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snippet_tags:
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- javascript.v3.bedrock-runtime.Converse_Mistral
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services:
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bedrock-runtime: {ConverseStream}
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bedrock-runtime_ConverseStream_AmazonTitanText:
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title: Invoke Amazon Titan Text on &BR; using Bedrock's Converse API with a response stream
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title_abbrev: "ConverseStream"
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bedrock-runtime: {InvokeModelWithResponseStream}
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# Image Generation Models
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bedrock-runtime_InvokeModel_AmazonNovaImageGeneration:
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title: Invoke Amazon Nova Canvas on &BR; to generate an image
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title_abbrev: "InvokeModel"
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synopsis: invoke Amazon Nova Canvas on &BR; to generate an image.
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category: Amazon Nova Canvas
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languages:
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JavaScript:
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versions:
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- sdk_version: 3
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github: javascriptv3/example_code/bedrock-runtime
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excerpts:
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- description: Create an image with Amazon Nova Canvas.
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snippet_tags:
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- javascript.v3.bedrock-runtime.InvokeModel_AmazonNovaImageGeneration
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services:
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bedrock-runtime: {InvokeModel}
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bedrock-runtime_InvokeModel_TitanImageGenerator:
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title: Invoke Amazon Titan Image on &BR; to generate an image
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title_abbrev: "InvokeModel"

‎javascriptv3/example_code/bedrock-runtime/.gitignore

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/tempx/
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/output/

‎javascriptv3/example_code/bedrock-runtime/README.md

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- [Converse](models/ai21LabsJurassic2/converse.js#L4)
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- [InvokeModel](models/ai21LabsJurassic2/invoke_model.js)
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### Amazon Nova
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- [Converse](models/amazonTitanText/converse.js#L4)
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- [ConverseStream](models/mistral/converse.js#L4)
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### Amazon Nova Canvas
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- [InvokeModel](models/amazonNovaCanvas/invokeModel.js#L4)
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### Amazon Titan Text
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- [Converse](models/amazonTitanText/converse.js#L4)

‎javascriptv3/example_code/bedrock-runtime/models/amazonNovaCanvas/invokeModel.js

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// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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// SPDX-License-Identifier: Apache-2.0
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// snippet-start:[javascript.v3.bedrock-runtime.InvokeModel_AmazonNovaImageGeneration]
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import {
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BedrockRuntimeClient,
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InvokeModelCommand,
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} from "@aws-sdk/client-bedrock-runtime";
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import { saveImage } from "../../utils/image-creation.js";
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import { fileURLToPath } from "node:url";
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/**
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* This example demonstrates how to use Amazon Nova Canvas to generate images.
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* It shows how to:
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* - Set up the Amazon Bedrock runtime client
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* - Configure the image generation parameters
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* - Send a request to generate an image
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* - Process the response and handle the generated image
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*
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* @returns {Promise<string>} Base64-encoded image data
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*/
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export const invokeModel = async () => {
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// Step 1: Create the Amazon Bedrock runtime client
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// Credentials will be automatically loaded from the environment
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const client = new BedrockRuntimeClient({ region: "us-east-1" });
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// Step 2: Specify which model to use
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// For the latest available models, see:
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// https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
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const modelId = "amazon.nova-canvas-v1:0";
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// Step 3: Configure the request payload
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// First, set the main parameters:
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// - prompt: Text description of the image to generate
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// - seed: Random number for reproducible generation (0 to 858,993,459)
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const prompt = "A stylized picture of a cute old steampunk robot";
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const seed = Math.floor(Math.random() * 858993460);
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// Then, create the payload using the following structure:
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// - taskType: TEXT_IMAGE (specifies text-to-image generation)
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// - textToImageParams: Contains the text prompt
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// - imageGenerationConfig: Contains optional generation settings (seed, quality, etc.)
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// For a list of available request parameters, see:
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// https://docs.aws.amazon.com/nova/latest/userguide/image-gen-req-resp-structure.html
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const payload = {
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taskType: "TEXT_IMAGE",
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textToImageParams: {
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text: prompt,
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},
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imageGenerationConfig: {
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seed,
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quality: "standard",
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},
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};
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// Step 4: Send and process the request
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// - Embed the payload in a request object
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// - Send the request to the model
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// - Extract and return the generated image data from the response
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try {
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const request = {
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modelId,
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body: JSON.stringify(payload),
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};
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const response = await client.send(new InvokeModelCommand(request));
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const decodedResponseBody = new TextDecoder().decode(response.body);
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// The response includes an array of base64-encoded PNG images
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/** @type {{images: string[]}} */
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const responseBody = JSON.parse(decodedResponseBody);
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return responseBody.images[0]; // Base64-encoded image data
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} catch (error) {
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console.error(`ERROR: Can't invoke '${modelId}'. Reason: ${error.message}`);
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throw error;
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}
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};
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// If run directly, execute the example and save the generated image
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if (process.argv[1] === fileURLToPath(import.meta.url)) {
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console.log("Generating image. This may take a few seconds...");
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invokeModel()
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.then(async (imageData) => {
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const imagePath = await saveImage(imageData, "nova-canvas");
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// Example path: javascriptv3/example_code/bedrock-runtime/output/nova-canvas/image-01.png
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console.log(`Image saved to: ${imagePath}`);
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})
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.catch((error) => {
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console.error("Execution failed:", error);
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process.exitCode = 1;
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});
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}
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// snippet-end:[javascript.v3.bedrock-runtime.InvokeModel_AmazonNovaImageGeneration]

‎javascriptv3/example_code/bedrock-runtime/models/amazonNovaText/converse.js

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// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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// SPDX-License-Identifier: Apache-2.0
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// snippet-start:[javascript.v3.bedrock-runtime.Converse_AmazonNovaText]
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// This example demonstrates how to use the Amazon Nova foundation models to generate text.
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// It shows how to:
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// - Set up the Amazon Bedrock runtime client
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// - Create a message
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// - Configure and send a request
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// - Process the response
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import {
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BedrockRuntimeClient,
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ConversationRole,
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ConverseCommand,
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} from "@aws-sdk/client-bedrock-runtime";
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// Step 1: Create the Amazon Bedrock runtime client
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// Credentials will be automatically loaded from the environment
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const client = new BedrockRuntimeClient({ region: "us-east-1" });
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// Step 2: Specify which model to use:
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// Available Amazon Nova models and their characteristics:
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// - Amazon Nova Micro: Text-only model optimized for lowest latency and cost
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// - Amazon Nova Lite: Fast, low-cost multimodal model for image, video, and text
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// - Amazon Nova Pro: Advanced multimodal model balancing accuracy, speed, and cost
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//
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// For the most current model IDs, see:
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// https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
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const modelId = "amazon.nova-lite-v1:0";
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// Step 3: Create the message
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// The message includes the text prompt and specifies that it comes from the user
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const inputText =
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"Describe the purpose of a 'hello world' program in one line.";
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const message = {
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content: [{ text: inputText }],
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role: ConversationRole.USER,
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};
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// Step 4: Configure the request
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// Optional parameters to control the model's response:
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// - maxTokens: maximum number of tokens to generate
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// - temperature: randomness (max: 1.0, default: 0.7)
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// OR
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// - topP: diversity of word choice (max: 1.0, default: 0.9)
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// Note: Use either temperature OR topP, but not both
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const request = {
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modelId,
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messages: [message],
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inferenceConfig: {
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maxTokens: 500, // The maximum response length
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temperature: 0.5, // Using temperature for randomness control
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//topP: 0.9, // Alternative: use topP instead of temperature
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},
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};
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// Step 5: Send and process the request
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// - Send the request to the model
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// - Extract and return the generated text from the response
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try {
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const response = await client.send(new ConverseCommand(request));
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console.log(response.output.message.content[0].text);
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} catch (error) {
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console.error(`ERROR: Can't invoke '${modelId}'. Reason: ${error.message}`);
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throw error;
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}
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// snippet-end:[javascript.v3.bedrock-runtime.Converse_AmazonNovaText]

‎javascriptv3/example_code/bedrock-runtime/models/amazonNovaText/converseStream.js

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// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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// SPDX-License-Identifier: Apache-2.0
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// snippet-start:[javascript.v3.bedrock-runtime.ConverseStream_AmazonNovaText]
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// This example demonstrates how to use the Amazon Nova foundation models
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// to generate streaming text responses.
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// It shows how to:
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// - Set up the Amazon Bedrock runtime client
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// - Create a message
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// - Configure a streaming request
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// - Process the streaming response
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import {
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BedrockRuntimeClient,
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ConversationRole,
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ConverseStreamCommand,
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} from "@aws-sdk/client-bedrock-runtime";
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// Step 1: Create the Amazon Bedrock runtime client
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// Credentials will be automatically loaded from the environment
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const client = new BedrockRuntimeClient({ region: "us-east-1" });
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// Step 2: Specify which model to use
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// Available Amazon Nova models and their characteristics:
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// - Amazon Nova Micro: Text-only model optimized for lowest latency and cost
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// - Amazon Nova Lite: Fast, low-cost multimodal model for image, video, and text
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// - Amazon Nova Pro: Advanced multimodal model balancing accuracy, speed, and cost
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//
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// For the most current model IDs, see:
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// https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
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const modelId = "amazon.nova-lite-v1:0";
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// Step 3: Create the message
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// The message includes the text prompt and specifies that it comes from the user
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const inputText =
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"Describe the purpose of a 'hello world' program in one paragraph";
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const message = {
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content: [{ text: inputText }],
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role: ConversationRole.USER,
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};
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// Step 4: Configure the streaming request
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// Optional parameters to control the model's response:
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// - maxTokens: maximum number of tokens to generate
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// - temperature: randomness (max: 1.0, default: 0.7)
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// OR
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// - topP: diversity of word choice (max: 1.0, default: 0.9)
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// Note: Use either temperature OR topP, but not both
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const request = {
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modelId,
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messages: [message],
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inferenceConfig: {
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maxTokens: 500, // The maximum response length
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temperature: 0.5, // Using temperature for randomness control
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//topP: 0.9, // Alternative: use topP instead of temperature
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},
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};
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// Step 5: Send and process the streaming request
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// - Send the request to the model
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// - Process each chunk of the streaming response
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try {
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const response = await client.send(new ConverseStreamCommand(request));
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for await (const chunk of response.stream) {
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if (chunk.contentBlockDelta) {
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// Print each text chunk as it arrives
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process.stdout.write(chunk.contentBlockDelta.delta?.text || "");
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}
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}
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} catch (error) {
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console.error(`ERROR: Can't invoke '${modelId}'. Reason: ${error.message}`);
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process.exitCode = 1;
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}
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// snippet-end:[javascript.v3.bedrock-runtime.ConverseStream_AmazonNovaText]

‎javascriptv3/example_code/bedrock-runtime/tests/converse.integration.test.js

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const baseDirectory = path.join(__dirname, "..", "models");
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const fileName = "converse.js";
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const subdirectories = [
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"ai21LabsJurassic2",
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"amazonTitanText",
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"anthropicClaude",
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"cohereCommand",
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"metaLlama",
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"mistral",
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];
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const models = {
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ai21LabsJurassic2: "AI21 Labs Jurassic-2",
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amazonNovaText: "Amazon Nova",
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amazonTitanText: "Amazon Titan",
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anthropicClaude: "Anthropic Claude",
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cohereCommand: "Cohere Command",
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metaLlama: "Meta Llama",
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mistral: "Mistral",
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};
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test.each(subdirectories)(
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"should invoke the model and return text",
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async (subdirectory) => {
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test.each(Object.entries(models).map(([sub, name]) => [name, sub]))(
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"should invoke %s and return text",
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async (_, subdirectory) => {
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const script = path.join(baseDirectory, subdirectory, fileName);
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const consoleLogSpy = vi.spyOn(console, "log");
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‎javascriptv3/example_code/bedrock-runtime/tests/converse_stream.integration.test.js

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const fileName = "converseStream.js";
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const baseDirectory = path.join(__dirname, "..", "models");
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const subdirectories = [
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"amazonTitanText",
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"anthropicClaude",
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"cohereCommand",
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"metaLlama",
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"mistral",
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];
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const models = {
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amazonNovaText: "Amazon Nova",
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amazonTitanText: "Amazon Titan",
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anthropicClaude: "Anthropic Claude",
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cohereCommand: "Cohere Command",
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metaLlama: "Meta Llama",
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mistral: "Mistral",
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};
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test.each(subdirectories)(
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"should invoke the model and return text",
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async (subdirectory) => {
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test.each(Object.entries(models).map(([sub, name]) => [name, sub]))(
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"should invoke %s and return text",
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async (_, subdirectory) => {
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let output = "";
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const outputStream = new Writable({
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write(/** @type string */ chunk, encoding, callback) {

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