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Add JavaScript examples for Amazon Nova and Amazon Nova Canvas
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DennisTraub committed Feb 19, 2025
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24 changes: 24 additions & 0 deletions .doc_gen/metadata/bedrock-runtime_metadata.yaml
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Expand Up @@ -86,6 +86,14 @@ bedrock-runtime_Converse_AmazonNovaText:
- description: Send a text message to Amazon Nova, using Bedrock's Converse API.
snippet_tags:
- bedrock-runtime.java2.Converse_AmazonNovaText
JavaScript:
versions:
- sdk_version: 3
github: javascriptv3/example_code/bedrock-runtime
excerpts:
- description: Send a text message to Amazon Nova, using Bedrock's Converse API.
snippet_tags:
- javascript.v3.bedrock-runtime.Converse_AmazonTitanText
services:
bedrock-runtime: {Converse}

Expand Down Expand Up @@ -335,6 +343,14 @@ bedrock-runtime_ConverseStream_AmazonNovaText:
- description: Send a text message to Amazon Nova using Bedrock's Converse API and process the response stream in real-time.
snippet_tags:
- bedrock-runtime.java2.ConverseStream_AmazonNovaText
JavaScript:
versions:
- sdk_version: 3
github: javascriptv3/example_code/bedrock-runtime
excerpts:
- description: Send a text message to Amazon Nova using Bedrock's Converse API and process the response stream in real-time.
snippet_tags:
- javascript.v3.bedrock-runtime.Converse_Mistral
services:
bedrock-runtime: {ConverseStream}

Expand Down Expand Up @@ -1123,6 +1139,14 @@ bedrock-runtime_InvokeModel_AmazonNovaImageGeneration:
- description: Create an image with Amazon Nova Canvas.
snippet_tags:
- bedrock-runtime.java2.InvokeModel_AmazonNovaImageGeneration
JavaScript:
versions:
- sdk_version: 3
github: javascriptv3/example_code/bedrock-runtime
excerpts:
- description: Create an image with Amazon Nova Canvas.
snippet_tags:
- javascript.v3.bedrock-runtime.InvokeModel_AmazonNovaImageGeneration
services:
bedrock-runtime: {InvokeModel}

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1 change: 1 addition & 0 deletions javascriptv3/example_code/bedrock-runtime/.gitignore
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@@ -1 +1,2 @@
/tempx/
/output/
9 changes: 9 additions & 0 deletions javascriptv3/example_code/bedrock-runtime/README.md
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Expand Up @@ -50,6 +50,15 @@ functions within the same service.
- [Converse](models/ai21LabsJurassic2/converse.js#L4)
- [InvokeModel](models/ai21LabsJurassic2/invoke_model.js)

### Amazon Nova

- [Converse](models/amazonTitanText/converse.js#L4)
- [ConverseStream](models/mistral/converse.js#L4)

### Amazon Nova Canvas

- [InvokeModel](models/amazonNovaCanvas/invokeModel.js#L4)

### Amazon Titan Text

- [Converse](models/amazonTitanText/converse.js#L4)
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// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: Apache-2.0

// snippet-start:[javascript.v3.bedrock-runtime.InvokeModel_AmazonNovaImageGeneration]

import {
BedrockRuntimeClient,
InvokeModelCommand,
} from "@aws-sdk/client-bedrock-runtime";
import { saveImage } from "../../utils/image-creation.js";
import { fileURLToPath } from "node:url";

/**
* This example demonstrates how to use Amazon Nova Canvas to generate images.
* It shows how to:
* - Set up the Amazon Bedrock runtime client
* - Configure the image generation parameters
* - Send a request to generate an image
* - Process the response and handle the generated image
*
* @returns {Promise<string>} Base64-encoded image data
*/
export const invokeModel = async () => {
// Step 1: Create the Amazon Bedrock runtime client
// Credentials will be automatically loaded from the environment
const client = new BedrockRuntimeClient({ region: "us-east-1" });

// Step 2: Specify which model to use
// For the latest available models, see:
// https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
const modelId = "amazon.nova-canvas-v1:0";

// Step 3: Configure the request payload
// First, set the main parameters:
// - prompt: Text description of the image to generate
// - seed: Random number for reproducible generation (0 to 858,993,459)
const prompt = "A stylized picture of a cute old steampunk robot";
const seed = Math.floor(Math.random() * 858993460);

// Then, create the payload using the following structure:
// - taskType: TEXT_IMAGE (specifies text-to-image generation)
// - textToImageParams: Contains the text prompt
// - imageGenerationConfig: Contains optional generation settings (seed, quality, etc.)
// For a list of available request parameters, see:
// https://docs.aws.amazon.com/nova/latest/userguide/image-gen-req-resp-structure.html
const payload = {
taskType: "TEXT_IMAGE",
textToImageParams: {
text: prompt,
},
imageGenerationConfig: {
seed,
quality: "standard",
},
};

// Step 4: Send and process the request
// - Embed the payload in a request object
// - Send the request to the model
// - Extract and return the generated image data from the response
try {
const request = {
modelId,
body: JSON.stringify(payload),
};
const response = await client.send(new InvokeModelCommand(request));

const decodedResponseBody = new TextDecoder().decode(response.body);
// The response includes an array of base64-encoded PNG images
/** @type {{images: string[]}} */
const responseBody = JSON.parse(decodedResponseBody);
return responseBody.images[0]; // Base64-encoded image data
} catch (error) {
console.error(`ERROR: Can't invoke '${modelId}'. Reason: ${error.message}`);
throw error;
}
};

// If run directly, execute the example and save the generated image
if (process.argv[1] === fileURLToPath(import.meta.url)) {
console.log("Generating image. This may take a few seconds...");
invokeModel()
.then(async (imageData) => {
const imagePath = await saveImage(imageData, "nova-canvas");
// Example path: javascriptv3/example_code/bedrock-runtime/output/nova-canvas/image-01.png
console.log(`Image saved to: ${imagePath}`);
})
.catch((error) => {
console.error("Execution failed:", error);
process.exitCode = 1;
});
}
// snippet-end:[javascript.v3.bedrock-runtime.InvokeModel_AmazonNovaImageGeneration]
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// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: Apache-2.0

// snippet-start:[javascript.v3.bedrock-runtime.Converse_AmazonNovaText]
// This example demonstrates how to use the Amazon Nova foundation models to generate text.
// It shows how to:
// - Set up the Amazon Bedrock runtime client
// - Create a message
// - Configure and send a request
// - Process the response

import {
BedrockRuntimeClient,
ConversationRole,
ConverseCommand,
} from "@aws-sdk/client-bedrock-runtime";

// Step 1: Create the Amazon Bedrock runtime client
// Credentials will be automatically loaded from the environment
const client = new BedrockRuntimeClient({ region: "us-east-1" });

// Step 2: Specify which model to use:
// Available Amazon Nova models and their characteristics:
// - Amazon Nova Micro: Text-only model optimized for lowest latency and cost
// - Amazon Nova Lite: Fast, low-cost multimodal model for image, video, and text
// - Amazon Nova Pro: Advanced multimodal model balancing accuracy, speed, and cost
//
// For the most current model IDs, see:
// https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
const modelId = "amazon.nova-lite-v1:0";

// Step 3: Create the message
// The message includes the text prompt and specifies that it comes from the user
const inputText =
"Describe the purpose of a 'hello world' program in one line.";
const message = {
content: [{ text: inputText }],
role: ConversationRole.USER,
};

// Step 4: Configure the request
// Optional parameters to control the model's response:
// - maxTokens: maximum number of tokens to generate
// - temperature: randomness (max: 1.0, default: 0.7)
// OR
// - topP: diversity of word choice (max: 1.0, default: 0.9)
// Note: Use either temperature OR topP, but not both
const request = {
modelId,
messages: [message],
inferenceConfig: {
maxTokens: 500, // The maximum response length
temperature: 0.5, // Using temperature for randomness control
//topP: 0.9, // Alternative: use topP instead of temperature
},
};

// Step 5: Send and process the request
// - Send the request to the model
// - Extract and return the generated text from the response
try {
const response = await client.send(new ConverseCommand(request));
console.log(response.output.message.content[0].text);
} catch (error) {
console.error(`ERROR: Can't invoke '${modelId}'. Reason: ${error.message}`);
throw error;
}
// snippet-end:[javascript.v3.bedrock-runtime.Converse_AmazonNovaText]
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// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: Apache-2.0

// snippet-start:[javascript.v3.bedrock-runtime.ConverseStream_AmazonNovaText]
// This example demonstrates how to use the Amazon Nova foundation models
// to generate streaming text responses.
// It shows how to:
// - Set up the Amazon Bedrock runtime client
// - Create a message
// - Configure a streaming request
// - Process the streaming response

import {
BedrockRuntimeClient,
ConversationRole,
ConverseStreamCommand,
} from "@aws-sdk/client-bedrock-runtime";

// Step 1: Create the Amazon Bedrock runtime client
// Credentials will be automatically loaded from the environment
const client = new BedrockRuntimeClient({ region: "us-east-1" });

// Step 2: Specify which model to use
// Available Amazon Nova models and their characteristics:
// - Amazon Nova Micro: Text-only model optimized for lowest latency and cost
// - Amazon Nova Lite: Fast, low-cost multimodal model for image, video, and text
// - Amazon Nova Pro: Advanced multimodal model balancing accuracy, speed, and cost
//
// For the most current model IDs, see:
// https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
const modelId = "amazon.nova-lite-v1:0";

// Step 3: Create the message
// The message includes the text prompt and specifies that it comes from the user
const inputText =
"Describe the purpose of a 'hello world' program in one paragraph";
const message = {
content: [{ text: inputText }],
role: ConversationRole.USER,
};

// Step 4: Configure the streaming request
// Optional parameters to control the model's response:
// - maxTokens: maximum number of tokens to generate
// - temperature: randomness (max: 1.0, default: 0.7)
// OR
// - topP: diversity of word choice (max: 1.0, default: 0.9)
// Note: Use either temperature OR topP, but not both
const request = {
modelId,
messages: [message],
inferenceConfig: {
maxTokens: 500, // The maximum response length
temperature: 0.5, // Using temperature for randomness control
//topP: 0.9, // Alternative: use topP instead of temperature
},
};

// Step 5: Send and process the streaming request
// - Send the request to the model
// - Process each chunk of the streaming response
try {
const response = await client.send(new ConverseStreamCommand(request));

for await (const chunk of response.stream) {
if (chunk.contentBlockDelta) {
// Print each text chunk as it arrives
process.stdout.write(chunk.contentBlockDelta.delta?.text || "");
}
}
} catch (error) {
console.error(`ERROR: Can't invoke '${modelId}'. Reason: ${error.message}`);
process.exitCode = 1;
}
// snippet-end:[javascript.v3.bedrock-runtime.ConverseStream_AmazonNovaText]
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Expand Up @@ -8,18 +8,19 @@ describe("Converse with text generation models", () => {
const baseDirectory = path.join(__dirname, "..", "models");
const fileName = "converse.js";

const subdirectories = [
"ai21LabsJurassic2",
"amazonTitanText",
"anthropicClaude",
"cohereCommand",
"metaLlama",
"mistral",
];
const models = {
ai21LabsJurassic2: "AI21 Labs Jurassic-2",
amazonNovaText: "Amazon Nova",
amazonTitanText: "Amazon Titan",
anthropicClaude: "Anthropic Claude",
cohereCommand: "Cohere Command",
metaLlama: "Meta Llama",
mistral: "Mistral",
};

test.each(subdirectories)(
"should invoke the model and return text",
async (subdirectory) => {
test.each(Object.entries(models).map(([sub, name]) => [name, sub]))(
"should invoke %s and return text",
async (_, subdirectory) => {
const script = path.join(baseDirectory, subdirectory, fileName);
const consoleLogSpy = vi.spyOn(console, "log");

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Expand Up @@ -9,17 +9,18 @@ describe("ConverseStream with text generation models", () => {
const fileName = "converseStream.js";
const baseDirectory = path.join(__dirname, "..", "models");

const subdirectories = [
"amazonTitanText",
"anthropicClaude",
"cohereCommand",
"metaLlama",
"mistral",
];
const models = {
amazonNovaText: "Amazon Nova",
amazonTitanText: "Amazon Titan",
anthropicClaude: "Anthropic Claude",
cohereCommand: "Cohere Command",
metaLlama: "Meta Llama",
mistral: "Mistral",
};

test.each(subdirectories)(
"should invoke the model and return text",
async (subdirectory) => {
test.each(Object.entries(models).map(([sub, name]) => [name, sub]))(
"should invoke %s and return text",
async (_, subdirectory) => {
let output = "";
const outputStream = new Writable({
write(/** @type string */ chunk, encoding, callback) {
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// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: Apache-2.0

import { describe, it } from "vitest";
import { invokeModel } from "../models/amazonNovaCanvas/invokeModel.js";
import { expectToBeANonEmptyString } from "./test_tools.js";

describe("Invoking Amazon Nova Canvas", () => {
it("should return a response", async () => {
const response = await invokeModel();
expectToBeANonEmptyString(response);
});
});
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Expand Up @@ -10,5 +10,5 @@ import { expect } from "vitest";
*/
export const expectToBeANonEmptyString = (string) => {
expect(typeof string).toBe("string");
expect(string.length).not.toBe(0);
expect(string).not.toHaveLength(0);
};
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