feat\!: migrate from TensorFlow.js to Transformers.js with ONNX Runtime
BREAKING CHANGE: Complete migration from TensorFlow.js to Transformers.js for embedding generation This is a major architectural change that replaces TensorFlow.js (USE model) with Transformers.js (all-MiniLM-L6-v2) for significantly improved performance and reduced complexity. Key Changes: - Replace TensorFlow.js Universal Sentence Encoder with Transformers.js all-MiniLM-L6-v2 - Reduce model size from 525MB to 87MB (83% reduction) - Reduce embedding dimensions from 512 to 384 (faster distance calculations) - Remove TensorFlow.js Float32Array patching (caused ONNX conflicts) - Implement smart bundled model detection for offline operation - Add explicit model download script for Docker deployments - Remove complex environment variables in favor of simple configuration - Update all distance functions to use optimized pure JavaScript - Remove TensorFlow-specific utilities and type definitions Performance Improvements: - Model loading: 5x faster (87MB vs 525MB) - Memory usage: 75% reduction (~200-400MB vs ~1.5GB) - Distance calculations: Faster pure JS vs GPU overhead for small vectors - Cold start performance: Significantly improved Files Changed: - Updated package.json: New dependencies, simplified scripts - Rewrote src/utils/embedding.ts: Complete Transformers.js implementation - Updated src/utils/distance.ts: Optimized JavaScript distance functions - Simplified src/setup.ts: Removed TensorFlow-specific patching - Simplified src/utils/textEncoding.ts: Only Node.js TextEncoder/Decoder patches - Deleted src/utils/robustModelLoader.ts: TensorFlow-specific loader - Deleted src/types/tensorflowTypes.ts: TensorFlow type definitions - Added scripts/download-models.cjs: Docker-compatible model downloader - Added comprehensive documentation: README.md, OFFLINE_MODELS.md, analysis docs Testing: - All 19 tests passing - Removed test mocking in favor of real implementation testing - Updated test environment for Transformers.js compatibility - Performance tests validate improved efficiency This migration resolves production issues with Docker egress limitations and provides a more robust, performant foundation for vector operations.
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36 changed files with 63263 additions and 2263 deletions
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@ -123,191 +123,90 @@ export async function calculateDistancesBatch(
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}
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try {
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// Function to be executed in a worker thread
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const distanceCalculator = async (args: {
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// Function for optimized batch distance calculation
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const distanceCalculator = (args: {
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queryVector: Vector
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vectors: Vector[]
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distanceFnString: string
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}) => {
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const { queryVector, vectors, distanceFnString } = args
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// Use TensorFlow.js with CPU processing
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const useTensorFlow = async () => {
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// TensorFlow.js will use its default EPSILON value
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// Optimized JavaScript implementations for different distance functions
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let distances: number[]
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// Use the importTensorFlow function if available (in worker context)
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// or directly import TensorFlow.js (in main thread)
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let tf
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if (
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typeof self !== 'undefined' &&
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typeof self.importTensorFlow === 'function'
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) {
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// In worker context, use the importTensorFlow function
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tf = await self.importTensorFlow()
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} else {
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// CRITICAL: Ensure TextEncoder/TextDecoder are available before TensorFlow.js loads
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try {
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// Use dynamic imports for all environments to ensure TensorFlow loads after patch
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if (typeof process !== 'undefined' && process.versions && process.versions.node) {
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// Ensure TextEncoder/TextDecoder are globally available in Node.js
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const util = await import('util')
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if (typeof global.TextEncoder === 'undefined') {
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global.TextEncoder = util.TextEncoder as unknown as typeof TextEncoder
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}
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if (typeof global.TextDecoder === 'undefined') {
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global.TextDecoder = util.TextDecoder as unknown as typeof TextDecoder
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}
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}
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// Apply the TensorFlow.js patch
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const { applyTensorFlowPatch } = await import('./textEncoding.js')
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await applyTensorFlowPatch()
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// Now load TensorFlow.js core module using dynamic imports
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tf = await import('@tensorflow/tfjs-core')
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await import('@tensorflow/tfjs-backend-cpu')
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await tf.setBackend('cpu')
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} catch (error) {
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console.error('Failed to initialize TensorFlow.js:', error)
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throw error
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if (distanceFnString.includes('euclideanDistance')) {
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// Euclidean distance: sqrt(sum((a - b)^2))
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distances = vectors.map((vector) => {
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let sum = 0
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for (let i = 0; i < queryVector.length; i++) {
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const diff = queryVector[i] - vector[i]
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sum += diff * diff
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}
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}
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// Convert vectors to tensors
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const queryTensor = tf.tensor2d([queryVector])
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const vectorsTensor = tf.tensor2d(vectors)
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let distances: number[]
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// Calculate distances based on the distance function type
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if (distanceFnString.includes('euclideanDistance')) {
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// Euclidean distance using GPU-optimized operations
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// Formula: sqrt(sum((a - b)^2))
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const expanded = tf.sub(
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(queryTensor as any).expandDims(1),
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(vectorsTensor as any).expandDims(0)
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)
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const squaredDiff = tf.square(expanded)
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const sumSquaredDiff = tf.sum(squaredDiff, -1)
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const distancesTensor = tf.sqrt(sumSquaredDiff)
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distances = (await (distancesTensor as any)
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.squeeze()
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.array()) as number[]
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// Clean up tensors
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queryTensor.dispose()
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vectorsTensor.dispose()
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expanded.dispose()
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squaredDiff.dispose()
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sumSquaredDiff.dispose()
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distancesTensor.dispose()
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} else if (distanceFnString.includes('cosineDistance')) {
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// Cosine distance using GPU-optimized operations
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// Formula: 1 - (a·b / (||a|| * ||b||))
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const dotProduct = tf.matMul(
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queryTensor,
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(vectorsTensor as any).transpose()
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)
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const queryNorm = tf.norm(queryTensor, 2, 1)
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const vectorsNorm = tf.norm(vectorsTensor, 2, 1)
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const normProduct = tf.outerProduct(
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queryNorm as any,
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vectorsNorm as any
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)
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const cosineSimilarity = tf.div(dotProduct, normProduct)
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const distancesTensor = tf.sub(tf.scalar(1), cosineSimilarity)
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distances = (await (distancesTensor as any)
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.squeeze()
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.array()) as number[]
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// Clean up tensors
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queryTensor.dispose()
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vectorsTensor.dispose()
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dotProduct.dispose()
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queryNorm.dispose()
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vectorsNorm.dispose()
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normProduct.dispose()
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cosineSimilarity.dispose()
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distancesTensor.dispose()
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} else if (distanceFnString.includes('manhattanDistance')) {
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// Manhattan distance using GPU-optimized operations
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// Formula: sum(|a - b|)
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const diff = tf.sub(
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(queryTensor as any).expandDims(1),
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(vectorsTensor as any).expandDims(0)
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)
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const absDiff = tf.abs(diff)
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const distancesTensor = tf.sum(absDiff, -1)
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distances = (await (distancesTensor as any)
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.squeeze()
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.array()) as number[]
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// Clean up tensors
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queryTensor.dispose()
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vectorsTensor.dispose()
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diff.dispose()
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absDiff.dispose()
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distancesTensor.dispose()
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} else if (distanceFnString.includes('dotProductDistance')) {
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// Dot product distance using GPU-optimized operations
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// Formula: -sum(a * b)
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const dotProduct = tf.matMul(
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queryTensor,
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(vectorsTensor as any).transpose()
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)
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const distancesTensor = tf.neg(dotProduct)
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distances = (await (distancesTensor as any)
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.squeeze()
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.array()) as number[]
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// Clean up tensors
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queryTensor.dispose()
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vectorsTensor.dispose()
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dotProduct.dispose()
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distancesTensor.dispose()
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} else {
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// For unknown distance functions, fall back to direct CPU implementation
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throw new Error(
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'Unsupported distance function for TensorFlow optimization'
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)
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}
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return {
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distances
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}
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}
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// Try to use TensorFlow.js with CPU optimization
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try {
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return await useTensorFlow()
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} catch (error) {
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// Fall back to direct CPU implementation if TensorFlow.js fails
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// Recreate the distance function from its string representation
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return Math.sqrt(sum)
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})
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} else if (distanceFnString.includes('cosineDistance')) {
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// Cosine distance: 1 - (a·b / (||a|| * ||b||))
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distances = vectors.map((vector) => {
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let dotProduct = 0
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let queryNorm = 0
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let vectorNorm = 0
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for (let i = 0; i < queryVector.length; i++) {
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dotProduct += queryVector[i] * vector[i]
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queryNorm += queryVector[i] * queryVector[i]
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vectorNorm += vector[i] * vector[i]
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}
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queryNorm = Math.sqrt(queryNorm)
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vectorNorm = Math.sqrt(vectorNorm)
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if (queryNorm === 0 || vectorNorm === 0) {
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return 1 // Maximum distance for zero vectors
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}
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const cosineSimilarity = dotProduct / (queryNorm * vectorNorm)
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return 1 - cosineSimilarity
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})
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} else if (distanceFnString.includes('manhattanDistance')) {
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// Manhattan distance: sum(|a - b|)
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distances = vectors.map((vector) => {
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let sum = 0
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for (let i = 0; i < queryVector.length; i++) {
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sum += Math.abs(queryVector[i] - vector[i])
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}
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return sum
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})
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} else if (distanceFnString.includes('dotProductDistance')) {
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// Dot product distance: -sum(a * b)
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distances = vectors.map((vector) => {
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let dotProduct = 0
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for (let i = 0; i < queryVector.length; i++) {
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dotProduct += queryVector[i] * vector[i]
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}
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return -dotProduct
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})
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} else {
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// For unknown distance functions, use the provided function
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const distanceFunction = new Function(
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'return ' + distanceFnString
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)() as DistanceFunction
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// Calculate distances for all vectors
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const distances = vectors.map((vector) =>
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distances = vectors.map((vector) =>
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distanceFunction(queryVector, vector)
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)
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return {
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distances
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}
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}
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return { distances }
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}
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// Threading is not available, so we'll always use the main thread implementation
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// This comment is kept for clarity about the removed code
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// Use the optimized distance calculator
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const result = distanceCalculator({
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queryVector,
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vectors,
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distanceFnString: distanceFunction.toString()
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})
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// If threading is not available or failed, calculate distances in the main thread
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return vectors.map((vector) => distanceFunction(queryVector, vector))
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return result.distances
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} catch (error) {
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// If anything fails, fall back to the standard distance function
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console.error('Batch distance calculation failed:', error)
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