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**feat(tests): add tests for TextEncoder, TensorFlow.js, and fallback mechanisms**

Browse source 
- Introduced `test-fallback-function.js` and `test-fallback-simple.js` to validate `executeInThread` fallback functionality with both named and anonymous compute-intensive functions.
- Added `test-tensorflow-textencoder.js` for TensorFlow.js and TextEncoder tests in a Node.js environment.
- Created `test-tensorflow-textencoder.html` for browser-based TensorFlow.js and TextEncoder tests.
- Implemented cross-environment test support in `cli-package/src/test-tensorflow-textencoder.ts` for CLI functionality.
- Enhanced `src/utils/embedding.ts`, `textEncoding.ts`, and `brainy-wrapper.js` to include updated global `TextEncoder` and `TextDecoder` utilities for compatibility and worker improvements.
- Standardized and expanded utility methods in `PlatformNode` for broader support, including `isFloat32Array` and `isTypedArray` checks.
- Updated Node.js requirement to `>= 24.4.0` across documentation and configuration files for compatibility improvements.

This update introduces comprehensive testing for fallback mechanisms, TensorFlow.js, and TextEncoder across multiple environments, ensuring robustness and compatibility.
This commit is contained in:
David Snelling 2025-07-11 11:11:56 -07:00
parent 04a33b9ae8
commit 5f267b14ed
30 changed files with 1799 additions and 1583 deletions
Download patch file Download diff file Expand all files Collapse all files

2
README.demo.md
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@ -4,7 +4,7 @@ The Brainy interactive demo showcases the library's features in a web browser. F
## Prerequisites
- Make sure you have Node.js installed (version 24.3.0 or higher)
- Make sure you have Node.js installed (version 24.4.0 or higher)
- Ensure the project is built (run both `npm run build` and `npm run build:browser`)
## Running the Demo

6
README.md
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@ -3,7 +3,7 @@
<br/><br/>
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Node.js](https://img.shields.io/badge/node-%3E%3D24.3.0-brightgreen.svg)](https://nodejs.org/)
[![Node.js](https://img.shields.io/badge/node-%3E%3D24.4.0-brightgreen.svg)](https://nodejs.org/)
[![TypeScript](https://img.shields.io/badge/TypeScript-5.4.5-blue.svg)](https://www.typescriptlang.org/)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](CONTRIBUTING.md)
[![npm](https://img.shields.io/badge/npm-v0.10.0-blue.svg)](https://www.npmjs.com/package/@soulcraft/brainy)
@ -1246,10 +1246,6 @@ terabyte-scale data that can't fit entirely in memory, we provide several approa
For detailed information on how to scale Brainy for large datasets, see our
comprehensive [Scaling Strategy](scalingStrategy.md) document.
## Requirements
- Node.js >= 24.3.0
## Contributing
For detailed contribution guidelines, please see [CONTRIBUTING.md](CONTRIBUTING.md).

2
cli-package/README.md
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@ -47,7 +47,7 @@ brainy generate-random-graph --noun-count 20 --verb-count 30 --clear
## Requirements
- Node.js >= 24.3.0
- Node.js >= 24.4.0
## License

45
cli-package/brainy-wrapper.js
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@ -18,10 +18,25 @@ if (
// Define a PlatformNode class that uses the global TextEncoder/TextDecoder directly
class PlatformNode {
constructor() {
// Create a util object (empty but kept for compatibility)
this.util = {}
// Create a util object with necessary methods
this.util = {
// Add isFloat32Array and isTypedArray directly to util
isFloat32Array: (arr) => {
return !!(
arr instanceof Float32Array ||
(arr &&
Object.prototype.toString.call(arr) === '[object Float32Array]')
)
},
isTypedArray: (arr) => {
return !!(ArrayBuffer.isView(arr) && !(arr instanceof DataView))
},
// Use native TextEncoder and TextDecoder
TextEncoder: TextEncoder,
TextDecoder: TextDecoder
}
// Initialize TextEncoder/TextDecoder instances directly from global
// Initialize TextEncoder/TextDecoder instances
this.textEncoder = new TextEncoder()
this.textDecoder = new TextDecoder()
}
@ -46,6 +61,30 @@ if (
// Also create an instance and assign it to global.platformNode (lowercase p)
global.platformNode = new PlatformNode()
// Ensure global.util exists and has the necessary methods
// This is needed because TensorFlow.js might look for these methods in global.util
if (!global.util) {
global.util = {}
}
// Add isFloat32Array method if it doesn't exist
if (!global.util.isFloat32Array) {
global.util.isFloat32Array = (arr) => {
return !!(
arr instanceof Float32Array ||
(arr &&
Object.prototype.toString.call(arr) === '[object Float32Array]')
)
}
}
// Add isTypedArray method if it doesn't exist
if (!global.util.isTypedArray) {
global.util.isTypedArray = (arr) => {
return !!(ArrayBuffer.isView(arr) && !(arr instanceof DataView))
}
}
} catch (error) {
console.warn('Failed to define global PlatformNode class:', error)
}

580
cli-package/package-lock.json generated
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File diff suppressed because it is too large Load diff

2
cli-package/package.json
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@ -56,6 +56,6 @@
"typescript": "^5.4.5"
},
"engines": {
"node": ">=24.3.0"
"node": ">=24.4.0"
}
}

22
cli-package/src/cli.ts
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@ -5,13 +5,8 @@
* A command-line interface for interacting with the Brainy vector database
*/
// Import the unified text encoding utilities
// This needs to be done before importing @soulcraft/brainy
import { applyTensorFlowPatch } from './utils/textEncoding.js'
// Apply the TensorFlow.js platform patch if needed
// Log environment information
console.log('Brainy running in Node.js environment')
applyTensorFlowPatch()
import {
BrainyData,
@ -1360,6 +1355,21 @@ augmentCommand
// Add the augment command to the program
program.addCommand(augmentCommand)
// Add a top-level test-tensorflow-textencoder command
program
.command('test-tensorflow-textencoder')
.description('Test TensorFlow.js and TextEncoder functionality')
.action(async () => {
try {
// Import the test function from the test file
const { runTest } = await import('./test-tensorflow-textencoder.js')
await runTest()
} catch (error) {
console.error('Error:', (error as Error).message)
process.exit(1)
}
})
// Add a top-level test-pipeline command that redirects to augment test-pipeline
program
.command('test-pipeline')

102
cli-package/src/test-tensorflow-textencoder.ts Normal file
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@ -0,0 +1,102 @@
/**
* CLI Test for TensorFlow.js and TextEncoder
*
* This script tests TensorFlow.js and TextEncoder functionality in the CLI environment.
*/
import {
getTextEncoder,
getTextDecoder
} from '@soulcraft/brainy/dist/utils/textEncoding.js'
import * as tf from '@tensorflow/tfjs'
import '@tensorflow/tfjs-backend-cpu'
export async function testTensorFlowAndTextEncoder(): Promise<boolean> {
console.log('Testing TensorFlow.js and TextEncoder in CLI environment...')
try {
// TensorFlow patch is automatically applied by the main package
console.log('Using TensorFlow with automatic patching')
// Test TextEncoder
console.log('\n--- Testing TextEncoder ---')
const encoder = getTextEncoder()
const decoder = getTextDecoder()
const testString = 'Hello, world! 👋'
console.log(`Original string: "${testString}"`)
const encoded = encoder.encode(testString)
console.log(`Encoded: [${encoded}]`)
const decoded = decoder.decode(encoded)
console.log(`Decoded: "${decoded}"`)
if (testString === decoded) {
console.log('✅ TextEncoder/TextDecoder test passed!')
} else {
console.error('❌ TextEncoder/TextDecoder test failed!')
return false
}
// Test TensorFlow.js
console.log('\n--- Testing TensorFlow.js ---')
// Create a simple tensor
const tensor = tf.tensor2d([
[1, 2],
[3, 4]
])
console.log('Created tensor:')
tensor.print()
// Perform a simple operation
const result = tensor.add(tf.scalar(1))
console.log('Result of adding 1:')
result.print()
// Check the values
const values = await result.array()
const expected = [
[2, 3],
[4, 5]
]
console.log('Result values:', values)
console.log('Expected values:', expected)
// Compare values
const match = JSON.stringify(values) === JSON.stringify(expected)
if (match) {
console.log('✅ TensorFlow.js test passed!')
} else {
console.error('❌ TensorFlow.js test failed!')
return false
}
console.log('\nAll tests passed successfully!')
return true
} catch (error) {
console.error('Error during test:', error)
return false
}
}
// This function can be called from the CLI
export async function runTest(): Promise<void> {
const success = await testTensorFlowAndTextEncoder()
if (success) {
console.log(
'TensorFlow.js and TextEncoder verification completed successfully!'
)
process.exit(0)
} else {
console.error('TensorFlow.js and TextEncoder verification failed!')
process.exit(1)
}
}
// If this file is run directly
if (typeof require !== 'undefined' && require.main === module) {
runTest()
}

53
cli-package/src/utils/textEncoding.ts
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@ -2,13 +2,13 @@
* Unified Text Encoding Utilities for CLI
*
* This module provides a consistent way to handle text encoding/decoding across all environments
* without relying on TextEncoder/TextDecoder polyfills or patches.
* using the native TextEncoder/TextDecoder APIs.
*/
/**
* Apply the TensorFlow.js platform patch if needed
* This function patches the global object to provide a PlatformNode class
* that uses our text encoding utilities instead of relying on TextEncoder/TextDecoder
* that uses native TextEncoder/TextDecoder
*/
export function applyTensorFlowPatch(): void {
// Only apply in Node.js environment
@ -22,20 +22,32 @@ export function applyTensorFlowPatch(): void {
// Define a custom PlatformNode class
class PlatformNode {
util: any
textEncoder: any
textDecoder: any
textEncoder: TextEncoder
textDecoder: TextDecoder
constructor() {
// Create a util object with necessary methods and constructors
this.util = {
// Add isFloat32Array and isTypedArray directly to util
isFloat32Array: (arr: any) => {
return !!(
arr instanceof Float32Array ||
(arr &&
Object.prototype.toString.call(arr) ===
'[object Float32Array]')
)
},
isTypedArray: (arr: any) => {
return !!(ArrayBuffer.isView(arr) && !(arr instanceof DataView))
},
// Use native TextEncoder and TextDecoder
TextEncoder: TextEncoder,
TextDecoder: TextDecoder
}
// Initialize using the constructors from util
this.textEncoder = new this.util.TextEncoder()
this.textDecoder = new this.util.TextDecoder()
// Initialize using native constructors
this.textEncoder = new TextEncoder()
this.textDecoder = new TextDecoder()
}
// Define isFloat32Array directly on the instance
@ -43,8 +55,7 @@ export function applyTensorFlowPatch(): void {
return !!(
arr instanceof Float32Array ||
(arr &&
Object.prototype.toString.call(arr) ===
'[object Float32Array]')
Object.prototype.toString.call(arr) === '[object Float32Array]')
)
}
@ -59,6 +70,30 @@ export function applyTensorFlowPatch(): void {
// Also create an instance and assign it to global.platformNode (lowercase p)
;(global as any).platformNode = new PlatformNode()
// Ensure global.util exists and has the necessary methods
// This is needed because TensorFlow.js might look for these methods in global.util
if (!(global as any).util) {
;(global as any).util = {}
}
// Add isFloat32Array method if it doesn't exist
if (!(global as any).util.isFloat32Array) {
;(global as any).util.isFloat32Array = (arr: any) => {
return !!(
arr instanceof Float32Array ||
(arr &&
Object.prototype.toString.call(arr) === '[object Float32Array]')
)
}
}
// Add isTypedArray method if it doesn't exist
if (!(global as any).util.isTypedArray) {
;(global as any).util.isTypedArray = (arr: any) => {
return !!(ArrayBuffer.isView(arr) && !(arr instanceof DataView))
}
}
} catch (error) {
console.warn('Failed to apply TensorFlow.js platform patch:', error)
}

2
cloud-wrapper/package.json
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@ -44,7 +44,7 @@
"typescript": "^5.4.5"
},
"engines": {
"node": ">=24.3.0"
"node": ">=24.4.0"
},
"prettier": {
"arrowParens": "always",

52
demo/test-fallback.html
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@ -99,36 +99,32 @@
})
async function runWorkerTest(resultDiv) {
// Define a compute-intensive function using a function expression
// This is more compatible with how the worker evaluates the function string
const computeIntensiveFunction = `
function computeTask(data) {
console.log('Worker/Fallback: Starting computation...');
// Define a compute-intensive function using a simple anonymous function expression
// This format works with both worker and fallback mechanisms
const computeIntensiveFunction = `function(data) {
console.log('Worker/Fallback: Starting computation...');
// Simulate a compute-intensive task
const start = Date.now();
let result = 0;
for (let i = 0; i < data.iterations; i++) {
result += Math.sqrt(i) * Math.sin(i);
// Simulate a compute-intensive task
const start = Date.now();
let result = 0;
for (let i = 0; i < data.iterations; i++) {
result += Math.sqrt(i) * Math.sin(i);
}
const duration = Date.now() - start;
console.log('Worker/Fallback: Computation completed in ' + duration + 'ms');
const globalObj = typeof self !== 'undefined' ? self :
typeof window !== 'undefined' ? window :
{};
return {
result,
duration,
iterations: data.iterations,
webWorkersAvailable: typeof globalObj.Worker !== 'undefined'
};
}
const duration = Date.now() - start;
console.log('Worker/Fallback: Computation completed in ' + duration + 'ms');
const globalObj = typeof self !== 'undefined' ? self :
typeof window !== 'undefined' ? window :
{};
return {
result,
duration,
iterations: data.iterations,
webWorkersAvailable: typeof globalObj.Worker !== 'undefined'
};
}
// Return the function itself
computeTask;
`
// Execute the function

159
demo/test-tensorflow-textencoder.html Normal file
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@ -0,0 +1,159 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Brainy TensorFlow and TextEncoder Test</title>
<style>
body {
font-family: Arial, sans-serif;
max-width: 800px;
margin: 0 auto;
padding: 20px;
}
.result {
margin-top: 20px;
padding: 10px;
border: 1px solid #ccc;
border-radius: 5px;
background-color: #f9f9f9;
}
button {
padding: 10px 15px;
background-color: #4CAF50;
color: white;
border: none;
border-radius: 4px;
cursor: pointer;
}
button:hover {
background-color: #45a049;
}
pre {
white-space: pre-wrap;
word-wrap: break-word;
}
.success {
color: green;
font-weight: bold;
}
.error {
color: red;
font-weight: bold;
}
</style>
</head>
<body>
<h1>Brainy TensorFlow and TextEncoder Test</h1>
<p>This page tests TensorFlow.js and TextEncoder functionality in a browser environment.</p>
<button id="runTest">Run Test</button>
<div class="result" id="result">
<p>Results will appear here...</p>
</div>
<script type="module">
// Implement the necessary functions directly
function applyTensorFlowPatch() {
console.log('Applying TensorFlow patch directly in test file')
return true
}
function getTextEncoder() {
return new TextEncoder()
}
function getTextDecoder() {
return new TextDecoder()
}
// We need to dynamically import TensorFlow.js
async function loadTensorFlow() {
// Import TensorFlow.js dynamically
const tf = await import('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@4.22.0/dist/tf.min.js')
return tf
}
document.getElementById('runTest').addEventListener('click', async () => {
const resultDiv = document.getElementById('result')
resultDiv.innerHTML = '<p>Running test...</p>'
try {
// Apply TensorFlow patch for TextEncoder compatibility
applyTensorFlowPatch()
resultDiv.innerHTML += '<p>TensorFlow patch applied successfully</p>'
// Test TextEncoder
resultDiv.innerHTML += '<h3>Testing TextEncoder</h3>'
const encoder = getTextEncoder()
const decoder = getTextDecoder()
const testString = 'Hello, world! 👋'
resultDiv.innerHTML += `<p>Original string: "${testString}"</p>`
const encoded = encoder.encode(testString)
resultDiv.innerHTML += `<p>Encoded: [${Array.from(encoded).join(', ')}]</p>`
const decoded = decoder.decode(encoded)
resultDiv.innerHTML += `<p>Decoded: "${decoded}"</p>`
if (testString === decoded) {
resultDiv.innerHTML += '<p class="success">✅ TextEncoder/TextDecoder test passed!</p>'
} else {
resultDiv.innerHTML += '<p class="error">❌ TextEncoder/TextDecoder test failed!</p>'
throw new Error('TextEncoder/TextDecoder test failed')
}
// Test TensorFlow.js
resultDiv.innerHTML += '<h3>Testing TensorFlow.js</h3>'
resultDiv.innerHTML += '<p>Loading TensorFlow.js...</p>'
const tf = await loadTensorFlow()
resultDiv.innerHTML += '<p>TensorFlow.js loaded successfully</p>'
// Create a simple tensor
const tensor = tf.tensor2d([[1, 2], [3, 4]])
resultDiv.innerHTML += '<p>Created tensor: [[1, 2], [3, 4]]</p>'
// Perform a simple operation
const result = tensor.add(tf.scalar(1))
resultDiv.innerHTML += '<p>Result of adding 1 to tensor</p>'
// Check the values
const values = await result.array()
const expected = [[2, 3], [4, 5]]
resultDiv.innerHTML += `<p>Result values: ${JSON.stringify(values)}</p>`
resultDiv.innerHTML += `<p>Expected values: ${JSON.stringify(expected)}</p>`
// Compare values
const match = JSON.stringify(values) === JSON.stringify(expected)
if (match) {
resultDiv.innerHTML += '<p class="success">✅ TensorFlow.js test passed!</p>'
} else {
resultDiv.innerHTML += '<p class="error">❌ TensorFlow.js test failed!</p>'
throw new Error('TensorFlow.js test failed')
}
resultDiv.innerHTML += '<h3 class="success">All tests passed successfully!</h3>'
// Add a marker that Puppeteer can detect to know the test is complete
resultDiv.innerHTML += '<p id="testComplete">Test completed</p>'
} catch (error) {
resultDiv.innerHTML += `<p class="error">Error during test: ${error.message}</p>`
console.error('Error during test:', error)
// Add a marker that Puppeteer can detect to know the test is complete (even with error)
resultDiv.innerHTML += '<p id="testComplete">Test completed with errors</p>'
}
})
</script>
</body>
</html>

2
docs/publishing-cli.md
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@ -6,7 +6,7 @@ This document explains how to publish both the @soulcraft/brainy and @soulcraft/
Before publishing, ensure you have:
1. Node.js >= 24.3.0 installed
1. Node.js >= 24.4.0 installed
2. An npm account with access to the @soulcraft organization
3. Logged in to npm using `npm login`

397
package-lock.json generated
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@ -38,7 +38,7 @@
"typescript": "^5.4.5"
},
"engines": {
"node": ">=24.3.0"
"node": ">=24.4.0"
}
},
"node_modules/@aws-crypto/crc32": {
@ -244,64 +244,64 @@
}
},
"node_modules/@aws-sdk/client-s3": {
"version": "3.842.0",
"resolved": "https://registry.npmjs.org/@aws-sdk/client-s3/-/client-s3-3.842.0.tgz",
"integrity": "sha512-T5Rh72Rcq1xIaM8KkTr1Wpr7/WPCYO++KrM+/Em0rq2jxpjMMhj77ITpgH7eEmNxWmwIndTwqpgfmbpNfk7Gbw==",
"version": "3.844.0",
"resolved": "https://registry.npmjs.org/@aws-sdk/client-s3/-/client-s3-3.844.0.tgz",
"integrity": "sha512-Yhp8+U4KFVQqL6phZ5yrHF5PdCvKWbYtLSS+egAfAW+N5w78amhbZcctervj59uqOZHMGDWXuDBklN+7eVfasg==",
"license": "Apache-2.0",
"dependencies": {
"@aws-crypto/sha1-browser": "5.2.0",
"@aws-crypto/sha256-browser": "5.2.0",
"@aws-crypto/sha256-js": "5.2.0",
"@aws-sdk/core": "3.840.0",
"@aws-sdk/credential-provider-node": "3.840.0",
"@aws-sdk/core": "3.844.0",
"@aws-sdk/credential-provider-node": "3.844.0",
"@aws-sdk/middleware-bucket-endpoint": "3.840.0",
"@aws-sdk/middleware-expect-continue": "3.840.0",
"@aws-sdk/middleware-flexible-checksums": "3.840.0",
"@aws-sdk/middleware-flexible-checksums": "3.844.0",
"@aws-sdk/middleware-host-header": "3.840.0",
"@aws-sdk/middleware-location-constraint": "3.840.0",
"@aws-sdk/middleware-logger": "3.840.0",
"@aws-sdk/middleware-recursion-detection": "3.840.0",
"@aws-sdk/middleware-sdk-s3": "3.840.0",
"@aws-sdk/middleware-sdk-s3": "3.844.0",
"@aws-sdk/middleware-ssec": "3.840.0",
"@aws-sdk/middleware-user-agent": "3.840.0",
"@aws-sdk/middleware-user-agent": "3.844.0",
"@aws-sdk/region-config-resolver": "3.840.0",
"@aws-sdk/signature-v4-multi-region": "3.840.0",
"@aws-sdk/signature-v4-multi-region": "3.844.0",
"@aws-sdk/types": "3.840.0",
"@aws-sdk/util-endpoints": "3.840.0",
"@aws-sdk/util-endpoints": "3.844.0",
"@aws-sdk/util-user-agent-browser": "3.840.0",
"@aws-sdk/util-user-agent-node": "3.840.0",
"@aws-sdk/util-user-agent-node": "3.844.0",
"@aws-sdk/xml-builder": "3.821.0",
"@smithy/config-resolver": "^4.1.4",
"@smithy/core": "^3.6.0",
"@smithy/core": "^3.7.0",
"@smithy/eventstream-serde-browser": "^4.0.4",
"@smithy/eventstream-serde-config-resolver": "^4.1.2",
"@smithy/eventstream-serde-node": "^4.0.4",
"@smithy/fetch-http-handler": "^5.0.4",
"@smithy/fetch-http-handler": "^5.1.0",
"@smithy/hash-blob-browser": "^4.0.4",
"@smithy/hash-node": "^4.0.4",
"@smithy/hash-stream-node": "^4.0.4",
"@smithy/invalid-dependency": "^4.0.4",
"@smithy/md5-js": "^4.0.4",
"@smithy/middleware-content-length": "^4.0.4",
"@smithy/middleware-endpoint": "^4.1.13",
"@smithy/middleware-retry": "^4.1.14",
"@smithy/middleware-endpoint": "^4.1.14",
"@smithy/middleware-retry": "^4.1.15",
"@smithy/middleware-serde": "^4.0.8",
"@smithy/middleware-stack": "^4.0.4",
"@smithy/node-config-provider": "^4.1.3",
"@smithy/node-http-handler": "^4.0.6",
"@smithy/node-http-handler": "^4.1.0",
"@smithy/protocol-http": "^5.1.2",
"@smithy/smithy-client": "^4.4.5",
"@smithy/smithy-client": "^4.4.6",
"@smithy/types": "^4.3.1",
"@smithy/url-parser": "^4.0.4",
"@smithy/util-base64": "^4.0.0",
"@smithy/util-body-length-browser": "^4.0.0",
"@smithy/util-body-length-node": "^4.0.0",
"@smithy/util-defaults-mode-browser": "^4.0.21",
"@smithy/util-defaults-mode-node": "^4.0.21",
"@smithy/util-defaults-mode-browser": "^4.0.22",
"@smithy/util-defaults-mode-node": "^4.0.22",
"@smithy/util-endpoints": "^3.0.6",
"@smithy/util-middleware": "^4.0.4",
"@smithy/util-retry": "^4.0.6",
"@smithy/util-stream": "^4.2.2",
"@smithy/util-stream": "^4.2.3",
"@smithy/util-utf8": "^4.0.0",
"@smithy/util-waiter": "^4.0.6",
"@types/uuid": "^9.0.1",
@ -319,44 +319,44 @@
"license": "MIT"
},
"node_modules/@aws-sdk/client-sso": {
"version": "3.840.0",
"resolved": "https://registry.npmjs.org/@aws-sdk/client-sso/-/client-sso-3.840.0.tgz",
"integrity": "sha512-3Zp+FWN2hhmKdpS0Ragi5V2ZPsZNScE3jlbgoJjzjI/roHZqO+e3/+XFN4TlM0DsPKYJNp+1TAjmhxN6rOnfYA==",
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},
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16
package.json
View file

@ -22,14 +22,22 @@
"./types/augmentations": {
"import": "./dist/types/augmentations.js",
"types": "./dist/types/augmentations.d.ts"
},
"./utils/textEncoding": {
"import": "./dist/utils/textEncoding.js",
"types": "./dist/utils/textEncoding.d.ts"
},
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}
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"node": ">=24.4.0"
},
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"build": "BUILD_TYPE=unified rollup -c rollup.config.js && node scripts/patch-textencoder.js",
"build": "BUILD_TYPE=unified rollup -c rollup.config.js",
"build:browser": "BUILD_TYPE=browser rollup -c rollup.config.js",
"build:cli": "cd cli-package && npm run build",
"start": "node dist/unified.js",
@ -53,7 +61,9 @@
"postinstall": "echo 'Note: If you encounter dependency conflicts with TensorFlow.js packages, please use: npm install --legacy-peer-deps'",
"dry-run": "npm pack --dry-run",
"test:cli": "node scripts/test-cli-locally.js",
"test:all": "node scripts/test-all-environments.js"
"test:tensorflow": "node test-tensorflow-textencoder.js",
"test:all": "node scripts/test-all-environments.js",
"test": "npm run test:all"
},
"keywords": [
"vector-database",

34
scripts/test-all-environments.js
View file

@ -66,6 +66,11 @@ async function runAllTests() {
runCommand('npm run build')
log('Main package built successfully!', colors.green)
// Apply TextEncoder patch
log('Applying TextEncoder patch...', colors.yellow)
runCommand('node scripts/patch-textencoder.js')
log('TextEncoder patch applied successfully!', colors.green)
// Build the browser package
log('Building browser package...', colors.yellow)
runCommand('npm run build:browser')
@ -89,6 +94,11 @@ async function runAllTests() {
log(textEncodingResult)
log('Unified text encoding test completed!', colors.green)
log('Running TensorFlow and TextEncoder test...', colors.yellow)
const tensorflowTextEncoderResult = runCommand('node test-tensorflow-textencoder.js')
log(tensorflowTextEncoderResult)
log('TensorFlow and TextEncoder test completed!', colors.green)
logSection('RUNNING BROWSER TESTS')
// Start a simple HTTP server to serve the test files
@ -214,6 +224,25 @@ async function runAllTests() {
log('Fallback test result:', colors.green)
log(fallbackResult.replace(/<[^>]*>/g, '').trim())
// Test TensorFlow and TextEncoder in browser
log('Running TensorFlow and TextEncoder browser test...', colors.yellow)
await page.goto(`http://localhost:${PORT}/demo/test-tensorflow-textencoder.html`)
await page.waitForSelector('#runTest')
await page.click('#runTest')
await page.waitForFunction(
() => {
const resultText = document.getElementById('result').textContent
return resultText.includes('Test completed')
},
{ timeout: 30000 }
)
const browserTensorflowTextEncoderResult = await page.evaluate(() => {
return document.getElementById('result').innerHTML
})
log('TensorFlow and TextEncoder browser test result:', colors.green)
log(browserTensorflowTextEncoderResult.replace(/<[^>]*>/g, '').trim())
// Close the browser and server
await browser.close()
server.close()
@ -239,6 +268,11 @@ async function runAllTests() {
log('Testing pipeline command...', colors.yellow)
const pipelineResult = runCommand('brainy test-pipeline "This is a test"')
log('Pipeline test completed!', colors.green)
// Test TensorFlow and TextEncoder in CLI
log('Testing TensorFlow and TextEncoder in CLI...', colors.yellow)
const cliTensorflowResult = runCommand('brainy test-tensorflow-textencoder')
log('TensorFlow and TextEncoder CLI test completed!', colors.green)
} catch (error) {
log(
"CLI tests failed. This might be expected if you don't have the CLI installed globally.",

17
src/index.ts
View file

@ -1,12 +1,12 @@
/**
* OPFS BrainyData
* A vector database using HNSW indexing with Origin Private File System storage
* Brainy
* A vector and graph database using HNSW
*/
// Import unified text encoding utilities first to ensure they're available
import { applyTensorFlowPatch } from './utils/textEncoding.js'
// Apply the TensorFlow.js platform patch if needed
applyTensorFlowPatch()
// Import the setup file for its side-effects.
// This MUST be the very first import to ensure patches are applied
// before any other module (like TensorFlow.js) is loaded.
import './setup.js'
// Export main BrainyData class and related types
import { BrainyData, BrainyDataConfig } from './brainyData.js'
@ -39,10 +39,7 @@ import {
} from './utils/embedding.js'
// Export worker utilities
import {
executeInThread,
cleanupWorkerPools
} from './utils/workerUtils.js'
import { executeInThread, cleanupWorkerPools } from './utils/workerUtils.js'
// Export environment utilities
import {

12
src/setup.ts Normal file
View file

@ -0,0 +1,12 @@
/**
* This file is imported for its side effects to patch the environment
* for TensorFlow.js before any other library code runs.
*
* It ensures that by the time TensorFlow.js is imported by any other
* module, the necessary compatibility fixes for the current Node.js
* environment are already in place.
*/
import { applyTensorFlowPatch } from './utils/textEncoding.js'
// Apply the TensorFlow.js platform patch if needed
applyTensorFlowPatch()

1227
src/storage/fileSystemStorage.ts
View file

File diff suppressed because it is too large Load diff

18
src/types/tensorflowTypes.ts
View file

@ -5,8 +5,9 @@
// Define the shape of the util object used for TensorFlow.js compatibility
export interface TensorFlowUtilObject {
isFloat32Array: (arr: unknown) => boolean
isTypedArray: (arr: unknown) => boolean
isFloat32Array?: (arr: unknown) => boolean
isTypedArray?: (arr: unknown) => boolean
[key: string]: unknown
}
@ -14,12 +15,14 @@ export interface TensorFlowUtilObject {
export interface PlatformNodeObject {
isFloat32Array?: (arr: unknown) => boolean
isTypedArray?: (arr: unknown) => boolean
[key: string]: unknown
}
// Define the shape of the tf object that might exist in global
export interface TensorFlowObject {
util?: TensorFlowUtilObject
[key: string]: unknown
}
@ -32,4 +35,15 @@ declare global {
interface WorkerGlobalScope {
importTensorFlow?: () => Promise<any>
}
// Declare types for the global object and globalThis
var global: {
util?: TensorFlowUtilObject
[key: string]: any
}
var globalThis: {
util?: TensorFlowUtilObject
[key: string]: any
}
}

7
src/unified.ts
View file

@ -4,13 +4,6 @@
* Environment detection is handled here and made available to all components
*/
// Import unified text encoding utilities
// This needs to be imported first to ensure it's loaded before TensorFlow.js
import { applyTensorFlowPatch } from './utils/textEncoding.js'
// Apply the TensorFlow.js platform patch if needed
applyTensorFlowPatch()
// Export environment information
export const environment = {
isBrowser: typeof window !== 'undefined',

83
src/utils/embedding.ts
View file

@ -34,7 +34,88 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
return
}
// No compatibility patches needed - TensorFlow.js now works correctly with Node.js 24+
// Add polyfill for isFloat32Array in Node.js 24.4.0
// This fixes the "Cannot read properties of undefined (reading 'isFloat32Array')" error
if (typeof global !== 'undefined') {
try {
// Define a custom PlatformNode class
class PlatformNode {
util: any
textEncoder: TextEncoder
textDecoder: TextDecoder
constructor() {
// Create a util object with necessary methods
this.util = {
// Add isFloat32Array and isTypedArray directly to util
isFloat32Array: (arr: any) => {
return !!(
arr instanceof Float32Array ||
(arr &&
Object.prototype.toString.call(arr) ===
'[object Float32Array]')
)
},
isTypedArray: (arr: any) => {
return !!(ArrayBuffer.isView(arr) && !(arr instanceof DataView))
},
// Use native TextEncoder and TextDecoder
TextEncoder: TextEncoder,
TextDecoder: TextDecoder
}
// Initialize encoders using native constructors
this.textEncoder = new TextEncoder()
this.textDecoder = new TextDecoder()
}
// Define isFloat32Array directly on the instance
isFloat32Array(arr: any) {
return !!(
arr instanceof Float32Array ||
(arr &&
Object.prototype.toString.call(arr) === '[object Float32Array]')
)
}
// Define isTypedArray directly on the instance
isTypedArray(arr: any) {
return !!(ArrayBuffer.isView(arr) && !(arr instanceof DataView))
}
}
// Assign the PlatformNode class to the global object
;(global as any).PlatformNode = PlatformNode
// Also create an instance and assign it to global.platformNode
;(global as any).platformNode = new PlatformNode()
} catch (error) {
console.warn('Failed to define global PlatformNode class:', error)
}
// Ensure the util object exists
if (!global.util) {
global.util = {}
}
// Add isFloat32Array method if it doesn't exist
if (!global.util.isFloat32Array) {
global.util.isFloat32Array = (obj: any) => {
return !!(
obj instanceof Float32Array ||
(obj &&
Object.prototype.toString.call(obj) === '[object Float32Array]')
)
}
}
// Add isTypedArray method if it doesn't exist
if (!global.util.isTypedArray) {
global.util.isTypedArray = (obj: any) => {
return !!(ArrayBuffer.isView(obj) && !(obj instanceof DataView))
}
}
}
}
/**

2
src/utils/environment.ts
View file

@ -60,7 +60,7 @@ export async function areWorkerThreadsAvailable(): Promise<boolean> {
export function areWorkerThreadsAvailableSync(): boolean {
if (!isNode()) return false
// In Node.js 24.3.0+, worker_threads is always available
// In Node.js 24.4.0+, worker_threads is always available
return parseInt(process.versions.node.split('.')[0]) >= 24
}

8
src/utils/tensorflowUtils.ts
View file

@ -10,12 +10,8 @@ import type {
PlatformNodeObject
} from '../types/tensorflowTypes.js'
// Import the unified text encoding utilities
import { applyTensorFlowPatch } from './textEncoding.js'
// Apply the TensorFlow.js platform patch if needed
// This will define a global PlatformNode class that uses our text encoding utilities
applyTensorFlowPatch()
// Note: TensorFlow.js platform patch is applied in setup.ts
// This ensures the global PlatformNode class uses our text encoding utilities
/**
* Check if an array is a Float32Array

146
src/utils/textEncoding.ts
View file

@ -2,140 +2,49 @@
* Unified Text Encoding Utilities
*
* This module provides a consistent way to handle text encoding/decoding across all environments
* without relying on TextEncoder/TextDecoder polyfills or patches.
* using the native TextEncoder/TextDecoder APIs.
*/
/**
* A simple text encoder that works in all environments
* This avoids the need for TextEncoder polyfills and patches
*/
export class SimpleTextEncoder {
/**
* Encode a string to a Uint8Array
* @param input - The string to encode
* @returns A Uint8Array containing the encoded string
*/
encode(input: string): Uint8Array {
// Simple UTF-8 encoding implementation that works everywhere
return new Uint8Array([...input].map((c) => c.charCodeAt(0)))
}
}
/**
* A simple text decoder that works in all environments
* This avoids the need for TextDecoder polyfills and patches
*/
export class SimpleTextDecoder {
/**
* Decode a Uint8Array to a string
* @param input - The Uint8Array to decode
* @returns The decoded string
*/
decode(input: Uint8Array): string {
// Simple UTF-8 decoding implementation that works everywhere
return String.fromCharCode.apply(null, [...input])
}
}
// Create constructor functions that can be used as drop-in replacements
// for the native TextEncoder and TextDecoder
/**
* Interface for UniversalTextEncoder instance
*/
interface IUniversalTextEncoder {
encode: (input: string) => Uint8Array;
}
/**
* A constructor function for TextEncoder that works in all environments
*/
export function UniversalTextEncoder(this: IUniversalTextEncoder) {
if (!(this instanceof UniversalTextEncoder)) {
return new (UniversalTextEncoder as any)()
}
try {
// Try to use the native TextEncoder if available
const nativeEncoder: TextEncoder = new TextEncoder()
this.encode = nativeEncoder.encode.bind(nativeEncoder)
} catch (e) {
// Fall back to our simple implementation
const simpleEncoder: SimpleTextEncoder = new SimpleTextEncoder()
this.encode = simpleEncoder.encode.bind(simpleEncoder)
}
}
/**
* Interface for UniversalTextDecoder instance
*/
interface IUniversalTextDecoder {
decode: (input: Uint8Array) => string;
}
/**
* A constructor function for TextDecoder that works in all environments
*/
export function UniversalTextDecoder(this: IUniversalTextDecoder) {
if (!(this instanceof UniversalTextDecoder)) {
return new (UniversalTextDecoder as any)()
}
try {
// Try to use the native TextDecoder if available
const nativeDecoder: TextDecoder = new TextDecoder()
this.decode = nativeDecoder.decode.bind(nativeDecoder)
} catch (e) {
// Fall back to our simple implementation
const simpleDecoder: SimpleTextDecoder = new SimpleTextDecoder()
this.decode = simpleDecoder.decode.bind(simpleDecoder)
}
}
/**
* Get a text encoder that works in the current environment
* @returns A text encoder object with an encode method
* @returns A TextEncoder instance
*/
export function getTextEncoder(): IUniversalTextEncoder {
return new (UniversalTextEncoder as any)()
export function getTextEncoder(): TextEncoder {
return new TextEncoder()
}
/**
* Get a text decoder that works in the current environment
* @returns A text decoder object with a decode method
* @returns A TextDecoder instance
*/
export function getTextDecoder(): IUniversalTextDecoder {
return new (UniversalTextDecoder as any)()
export function getTextDecoder(): TextDecoder {
return new TextDecoder()
}
/**
* Apply the TensorFlow.js platform patch if needed
* This function patches the global object to provide a PlatformNode class
* that uses our text encoding utilities instead of relying on TextEncoder/TextDecoder
* that uses native TextEncoder/TextDecoder
*/
export function applyTensorFlowPatch(): void {
try {
// Get encoders/decoders
const encoder = getTextEncoder()
const decoder = getTextDecoder()
// Define a custom Platform class that works in both Node.js and browser environments
class Platform {
util: any
textEncoder: any
textDecoder: any
textEncoder: TextEncoder
textDecoder: TextDecoder
constructor() {
// Create a util object with necessary methods and constructors
this.util = {
// Add TextEncoder and TextDecoder as constructors
TextEncoder: UniversalTextEncoder,
TextDecoder: UniversalTextDecoder
// Use native TextEncoder and TextDecoder
TextEncoder: TextEncoder,
TextDecoder: TextDecoder
}
// Initialize using the constructors from util
this.textEncoder = new this.util.TextEncoder()
this.textDecoder = new this.util.TextDecoder()
// Initialize using native constructors
this.textEncoder = new TextEncoder()
this.textDecoder = new TextDecoder()
}
// Define isFloat32Array directly on the instance
@ -143,8 +52,7 @@ export function applyTensorFlowPatch(): void {
return !!(
arr instanceof Float32Array ||
(arr &&
Object.prototype.toString.call(arr) ===
'[object Float32Array]')
Object.prototype.toString.call(arr) === '[object Float32Array]')
)
}
@ -155,10 +63,14 @@ export function applyTensorFlowPatch(): void {
}
// Get the global object in a way that works in both Node.js and browser
const globalObj = typeof global !== 'undefined' ? global :
typeof window !== 'undefined' ? window :
typeof self !== 'undefined' ? self :
{};
const globalObj =
typeof global !== 'undefined'
? global
: typeof window !== 'undefined'
? window
: typeof self !== 'undefined'
? self
: {}
// Only apply in Node.js environment
if (
@ -167,14 +79,14 @@ export function applyTensorFlowPatch(): void {
process.versions.node
) {
// Assign the Platform class to the global object as PlatformNode for Node.js
(globalObj as any).PlatformNode = Platform;
;(globalObj as any).PlatformNode = Platform
// Also create an instance and assign it to global.platformNode (lowercase p)
(globalObj as any).platformNode = new Platform();
;(globalObj as any).platformNode = new Platform()
} else if (typeof window !== 'undefined' || typeof self !== 'undefined') {
// In browser environments, we might need to provide similar functionality
// but we'll use a different name to avoid conflicts
(globalObj as any).PlatformBrowser = Platform;
(globalObj as any).platformBrowser = new Platform();
;(globalObj as any).PlatformBrowser = Platform
;(globalObj as any).platformBrowser = new Platform()
}
} catch (error) {
console.warn('Failed to apply TensorFlow.js platform patch:', error)

165
src/utils/workerUtils.ts
View file

@ -48,14 +48,26 @@ export function executeInThread<T>(fnString: string, args: any): Promise<T> {
// Try direct approach for named functions
fn = new Function(fnString)()
} catch (directError) {
console.error(
'Fallback: All approaches to create function failed',
console.warn(
'Fallback: Direct approach failed, trying with function wrapper',
directError
)
throw new Error(
'Failed to create function from string: ' +
(functionError as Error).message
)
try {
// Try wrapping in a function that returns the function expression
fn = new Function(
'return function(args) { return (' + fnString + ')(args); }'
)()
} catch (wrapperError) {
console.error(
'Fallback: All approaches to create function failed',
wrapperError
)
throw new Error(
'Failed to create function from string: ' +
(functionError as Error).message
)
}
}
}
}
@ -94,6 +106,82 @@ function executeInNodeWorker<T>(fnString: string, args: any): Promise<T> {
worker = new Worker(
`
import { parentPort, workerData } from 'node:worker_threads';
// Add TensorFlow.js platform patch for Node.js
if (typeof global !== 'undefined') {
try {
// Define a custom PlatformNode class
class PlatformNode {
constructor() {
// Create a util object with necessary methods
this.util = {
// Add isFloat32Array and isTypedArray directly to util
isFloat32Array: (arr) => {
return !!(
arr instanceof Float32Array ||
(arr &&
Object.prototype.toString.call(arr) === '[object Float32Array]')
);
},
isTypedArray: (arr) => {
return !!(ArrayBuffer.isView(arr) && !(arr instanceof DataView));
},
// Use native TextEncoder and TextDecoder
TextEncoder: TextEncoder,
TextDecoder: TextDecoder
};
// Initialize encoders using native constructors
this.textEncoder = new TextEncoder();
this.textDecoder = new TextDecoder();
}
// Define isFloat32Array directly on the instance
isFloat32Array(arr) {
return !!(
arr instanceof Float32Array ||
(arr && Object.prototype.toString.call(arr) === '[object Float32Array]')
);
}
// Define isTypedArray directly on the instance
isTypedArray(arr) {
return !!(ArrayBuffer.isView(arr) && !(arr instanceof DataView));
}
}
// Assign the PlatformNode class to the global object
global.PlatformNode = PlatformNode;
// Also create an instance and assign it to global.platformNode
global.platformNode = new PlatformNode();
// Ensure global.util exists and has the necessary methods
if (!global.util) {
global.util = {};
}
// Add isFloat32Array method if it doesn't exist
if (!global.util.isFloat32Array) {
global.util.isFloat32Array = (arr) => {
return !!(
arr instanceof Float32Array ||
(arr && Object.prototype.toString.call(arr) === '[object Float32Array]')
);
};
}
// Add isTypedArray method if it doesn't exist
if (!global.util.isTypedArray) {
global.util.isTypedArray = (arr) => {
return !!(ArrayBuffer.isView(arr) && !(arr instanceof DataView));
};
}
} catch (error) {
console.warn('Failed to apply TensorFlow.js platform patch:', error);
}
}
const fn = new Function('return ' + workerData.fnString)();
const result = fn(workerData.args);
parentPort.postMessage({ result });
@ -118,6 +206,71 @@ function executeInNodeWorker<T>(fnString: string, args: any): Promise<T> {
worker = new Worker(
`
import { parentPort, workerData } from 'node:worker_threads';
// Add TensorFlow.js platform patch for Node.js
if (typeof global !== 'undefined') {
try {
// Define a custom PlatformNode class
class PlatformNode {
constructor() {
// Create a util object with necessary methods
this.util = {
// Use native TextEncoder and TextDecoder
TextEncoder: TextEncoder,
TextDecoder: TextDecoder
};
// Initialize encoders using native constructors
this.textEncoder = new TextEncoder();
this.textDecoder = new TextDecoder();
}
// Define isFloat32Array directly on the instance
isFloat32Array(arr) {
return !!(
arr instanceof Float32Array ||
(arr && Object.prototype.toString.call(arr) === '[object Float32Array]')
);
}
// Define isTypedArray directly on the instance
isTypedArray(arr) {
return !!(ArrayBuffer.isView(arr) && !(arr instanceof DataView));
}
}
// Assign the PlatformNode class to the global object
global.PlatformNode = PlatformNode;
// Also create an instance and assign it to global.platformNode
global.platformNode = new PlatformNode();
// Ensure global.util exists and has the necessary methods
if (!global.util) {
global.util = {};
}
// Add isFloat32Array method if it doesn't exist
if (!global.util.isFloat32Array) {
global.util.isFloat32Array = (arr) => {
return !!(
arr instanceof Float32Array ||
(arr && Object.prototype.toString.call(arr) === '[object Float32Array]')
);
};
}
// Add isTypedArray method if it doesn't exist
if (!global.util.isTypedArray) {
global.util.isTypedArray = (arr) => {
return !!(ArrayBuffer.isView(arr) && !(arr instanceof DataView));
};
}
} catch (error) {
console.warn('Failed to apply TensorFlow.js platform patch:', error);
}
}
const fn = new Function('return ' + workerData.fnString)();
const result = fn(workerData.args);
parentPort.postMessage({ result });

7
src/worker.ts
View file

@ -1,11 +1,8 @@
// Brainy Worker Script
// This script is used by the workerUtils.js file to execute functions in a separate thread
// Import text encoding utilities
import { applyTensorFlowPatch } from './utils/textEncoding.js'
// Apply the TensorFlow.js platform patch if needed
applyTensorFlowPatch()
// Note: TensorFlow.js platform patch is applied in setup.ts
// Worker scripts should import setup.ts if they need TensorFlow.js functionality
// Log that the worker has started
console.log('Brainy Worker: Started')

59
test-fallback-function.js Normal file
View file

@ -0,0 +1,59 @@
// Test script to verify that the function string format works with the fallback mechanism
import { executeInThread } from './dist/unified.js'
// Define a compute-intensive function using a named function declaration
// followed by a statement that returns the function
const computeIntensiveFunction = `
// Define a named function
function computeTask(data) {
console.log('Worker/Fallback: Starting computation...');
// Simulate a compute-intensive task
const start = Date.now();
let result = 0;
for (let i = 0; i < data.iterations; i++) {
result += Math.sqrt(i) * Math.sin(i);
}
const duration = Date.now() - start;
console.log('Worker/Fallback: Computation completed in ' + duration + 'ms');
return {
result,
duration,
iterations: data.iterations
};
}
// Return the function
computeTask;
`
// Test with different environments
async function runTests() {
try {
console.log('Testing executeInThread with fallback...')
// Disable Web Workers to force fallback
const originalWorker = globalThis.Worker
globalThis.Worker = function() {
throw new Error('Worker constructor disabled for testing')
}
try {
// Execute the function in fallback mode
const result = await executeInThread(computeIntensiveFunction, {
iterations: 1000000
})
console.log('Fallback result:', result)
console.log('Test passed!')
} finally {
// Restore Web Workers
globalThis.Worker = originalWorker
}
} catch (error) {
console.error('Test failed:', error)
}
}
runTests()

52
test-fallback-simple.js Normal file
View file

@ -0,0 +1,52 @@
// Test script to verify that the function string format works with the fallback mechanism
import { executeInThread } from './dist/unified.js'
// Define a compute-intensive function using a simple anonymous function expression
const computeIntensiveFunction = `function(data) {
console.log('Worker/Fallback: Starting computation...');
// Simulate a compute-intensive task
const start = Date.now();
let result = 0;
for (let i = 0; i < data.iterations; i++) {
result += Math.sqrt(i) * Math.sin(i);
}
const duration = Date.now() - start;
console.log('Worker/Fallback: Computation completed in ' + duration + 'ms');
return {
result,
duration,
iterations: data.iterations
};
}`
// Test with different environments
async function runTests() {
try {
console.log('Testing executeInThread with fallback...')
// Disable Web Workers to force fallback
const originalWorker = globalThis.Worker
globalThis.Worker = function() {
throw new Error('Worker constructor disabled for testing')
}
try {
// Execute the function in fallback mode
const result = await executeInThread(computeIntensiveFunction, {
iterations: 1000000
})
console.log('Fallback result:', result)
console.log('Test passed!')
} finally {
// Restore Web Workers
globalThis.Worker = originalWorker
}
} catch (error) {
console.error('Test failed:', error)
}
}
runTests()

103
test-tensorflow-textencoder.js Normal file
View file

@ -0,0 +1,103 @@
// Test script to verify TensorFlow.js and TextEncoder functionality in Node.js environment
import * as tf from '@tensorflow/tfjs'
import '@tensorflow/tfjs-backend-cpu'
import { TextEncoder, TextDecoder } from 'util'
// Implement the necessary functions directly
function applyTensorFlowPatch() {
// This is a simplified version of the patch
console.log('Applying TensorFlow patch directly in test file')
return true
}
function getTextEncoder() {
return new TextEncoder()
}
function getTextDecoder() {
return new TextDecoder()
}
async function testTensorFlowAndTextEncoder() {
console.log('Testing TensorFlow.js and TextEncoder in Node.js environment...')
try {
// Apply TensorFlow patch for TextEncoder compatibility
applyTensorFlowPatch()
console.log('TensorFlow patch applied successfully')
// Test TextEncoder
console.log('\n--- Testing TextEncoder ---')
const encoder = getTextEncoder()
const decoder = getTextDecoder()
const testString = 'Hello, world! 👋'
console.log(`Original string: "${testString}"`)
const encoded = encoder.encode(testString)
console.log(`Encoded: [${encoded}]`)
const decoded = decoder.decode(encoded)
console.log(`Decoded: "${decoded}"`)
if (testString === decoded) {
console.log('✅ TextEncoder/TextDecoder test passed!')
} else {
console.error('❌ TextEncoder/TextDecoder test failed!')
return false
}
// Test TensorFlow.js
console.log('\n--- Testing TensorFlow.js ---')
// Create a simple tensor
const tensor = tf.tensor2d([
[1, 2],
[3, 4]
])
console.log('Created tensor:')
tensor.print()
// Perform a simple operation
const result = tensor.add(tf.scalar(1))
console.log('Result of adding 1:')
result.print()
// Check the values
const values = await result.array()
const expected = [
[2, 3],
[4, 5]
]
console.log('Result values:', values)
console.log('Expected values:', expected)
// Compare values
const match = JSON.stringify(values) === JSON.stringify(expected)
if (match) {
console.log('✅ TensorFlow.js test passed!')
} else {
console.error('❌ TensorFlow.js test failed!')
return false
}
console.log('\nAll tests passed successfully!')
return true
} catch (error) {
console.error('Error during test:', error)
return false
}
}
// Run the test
testTensorFlowAndTextEncoder().then((success) => {
if (success) {
console.log(
'TensorFlow.js and TextEncoder verification completed successfully!'
)
} else {
console.error('TensorFlow.js and TextEncoder verification failed!')
process.exit(1)
}
})