Initial commit: Brainy - Multi-Dimensional AI Database
Open source vector database with HNSW indexing, graph relationships, and metadata facets. Features CLI with professional augmentation registry integration for discovering extensions and capabilities.
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471
dist/hnsw/hnswIndexOptimized.js
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471
dist/hnsw/hnswIndexOptimized.js
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/**
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* Optimized HNSW (Hierarchical Navigable Small World) Index implementation
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* Extends the base HNSW implementation with support for large datasets
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* Uses product quantization for dimensionality reduction and disk-based storage when needed
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*/
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import { HNSWIndex } from './hnswIndex.js';
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// Default configuration for the optimized HNSW index
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const DEFAULT_OPTIMIZED_CONFIG = {
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M: 16,
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efConstruction: 200,
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efSearch: 50,
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ml: 16,
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memoryThreshold: 1024 * 1024 * 1024, // 1GB default threshold
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productQuantization: {
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enabled: false,
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numSubvectors: 16,
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numCentroids: 256
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},
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useDiskBasedIndex: false
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};
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/**
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* Product Quantization implementation
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* Reduces vector dimensionality by splitting vectors into subvectors
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* and quantizing each subvector to the nearest centroid
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*/
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class ProductQuantizer {
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constructor(numSubvectors = 16, numCentroids = 256) {
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this.centroids = [];
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this.subvectorSize = 0;
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this.initialized = false;
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this.dimension = 0;
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this.numSubvectors = numSubvectors;
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this.numCentroids = numCentroids;
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}
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/**
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* Initialize the product quantizer with training data
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* @param vectors Training vectors to use for learning centroids
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*/
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train(vectors) {
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if (vectors.length === 0) {
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throw new Error('Cannot train product quantizer with empty vector set');
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}
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this.dimension = vectors[0].length;
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this.subvectorSize = Math.ceil(this.dimension / this.numSubvectors);
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// Initialize centroids for each subvector
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for (let i = 0; i < this.numSubvectors; i++) {
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// Extract subvectors from training data
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const subvectors = vectors.map((vector) => {
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const start = i * this.subvectorSize;
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const end = Math.min(start + this.subvectorSize, this.dimension);
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return vector.slice(start, end);
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});
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// Initialize centroids for this subvector using k-means++
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this.centroids[i] = this.kMeansPlusPlus(subvectors, this.numCentroids);
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}
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this.initialized = true;
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}
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/**
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* Quantize a vector using product quantization
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* @param vector Vector to quantize
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* @returns Array of centroid indices, one for each subvector
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*/
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quantize(vector) {
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if (!this.initialized) {
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throw new Error('Product quantizer not initialized. Call train() first.');
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}
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if (vector.length !== this.dimension) {
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throw new Error(`Vector dimension mismatch: expected ${this.dimension}, got ${vector.length}`);
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}
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const codes = [];
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// Quantize each subvector
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for (let i = 0; i < this.numSubvectors; i++) {
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const start = i * this.subvectorSize;
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const end = Math.min(start + this.subvectorSize, this.dimension);
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const subvector = vector.slice(start, end);
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// Find nearest centroid
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let minDist = Number.MAX_VALUE;
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let nearestCentroidIndex = 0;
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for (let j = 0; j < this.centroids[i].length; j++) {
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const centroid = this.centroids[i][j];
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const dist = this.euclideanDistanceSquared(subvector, centroid);
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if (dist < minDist) {
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minDist = dist;
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nearestCentroidIndex = j;
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}
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}
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codes.push(nearestCentroidIndex);
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}
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return codes;
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}
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/**
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* Reconstruct a vector from its quantized representation
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* @param codes Array of centroid indices
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* @returns Reconstructed vector
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*/
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reconstruct(codes) {
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if (!this.initialized) {
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throw new Error('Product quantizer not initialized. Call train() first.');
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}
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if (codes.length !== this.numSubvectors) {
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throw new Error(`Code length mismatch: expected ${this.numSubvectors}, got ${codes.length}`);
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}
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const reconstructed = [];
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// Reconstruct each subvector
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for (let i = 0; i < this.numSubvectors; i++) {
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const centroidIndex = codes[i];
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const centroid = this.centroids[i][centroidIndex];
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// Add centroid components to reconstructed vector
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for (const component of centroid) {
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reconstructed.push(component);
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}
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}
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// Trim to original dimension if needed
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return reconstructed.slice(0, this.dimension);
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}
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/**
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* Compute squared Euclidean distance between two vectors
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* @param a First vector
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* @param b Second vector
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* @returns Squared Euclidean distance
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*/
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euclideanDistanceSquared(a, b) {
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let sum = 0;
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const length = Math.min(a.length, b.length);
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for (let i = 0; i < length; i++) {
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const diff = a[i] - b[i];
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sum += diff * diff;
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}
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return sum;
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}
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/**
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* Implement k-means++ algorithm to initialize centroids
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* @param vectors Vectors to cluster
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* @param k Number of clusters
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* @returns Array of centroids
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*/
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kMeansPlusPlus(vectors, k) {
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if (vectors.length < k) {
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// If we have fewer vectors than centroids, use the vectors as centroids
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return [...vectors];
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}
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const centroids = [];
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// Choose first centroid randomly
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const firstIndex = Math.floor(Math.random() * vectors.length);
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centroids.push([...vectors[firstIndex]]);
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// Choose remaining centroids
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for (let i = 1; i < k; i++) {
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// Compute distances to nearest centroid for each vector
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const distances = vectors.map((vector) => {
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let minDist = Number.MAX_VALUE;
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for (const centroid of centroids) {
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const dist = this.euclideanDistanceSquared(vector, centroid);
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minDist = Math.min(minDist, dist);
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}
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return minDist;
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});
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// Compute sum of distances
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const distSum = distances.reduce((sum, dist) => sum + dist, 0);
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// Choose next centroid with probability proportional to distance
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let r = Math.random() * distSum;
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let nextIndex = 0;
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for (let j = 0; j < distances.length; j++) {
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r -= distances[j];
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if (r <= 0) {
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nextIndex = j;
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break;
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}
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}
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centroids.push([...vectors[nextIndex]]);
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}
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return centroids;
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}
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/**
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* Get the centroids for each subvector
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* @returns Array of centroid arrays
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*/
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getCentroids() {
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return this.centroids;
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}
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/**
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* Set the centroids for each subvector
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* @param centroids Array of centroid arrays
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*/
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setCentroids(centroids) {
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this.centroids = centroids;
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this.numSubvectors = centroids.length;
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this.numCentroids = centroids[0].length;
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this.initialized = true;
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}
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/**
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* Get the dimension of the vectors
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* @returns Dimension
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*/
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getDimension() {
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return this.dimension;
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}
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/**
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* Set the dimension of the vectors
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* @param dimension Dimension
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*/
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setDimension(dimension) {
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this.dimension = dimension;
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this.subvectorSize = Math.ceil(dimension / this.numSubvectors);
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}
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}
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/**
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* Optimized HNSW Index implementation
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* Extends the base HNSW implementation with support for large datasets
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* Uses product quantization for dimensionality reduction and disk-based storage when needed
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*/
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export class HNSWIndexOptimized extends HNSWIndex {
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constructor(config = {}, distanceFunction, storage = null) {
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// Initialize base HNSW index with standard config
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super(config, distanceFunction);
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this.productQuantizer = null;
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this.storage = null;
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this.useDiskBasedIndex = false;
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this.useProductQuantization = false;
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this.quantizedVectors = new Map();
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this.memoryUsage = 0;
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this.vectorCount = 0;
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// Thread safety for memory usage tracking
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this.memoryUpdateLock = Promise.resolve();
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// Set optimized config
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this.optimizedConfig = { ...DEFAULT_OPTIMIZED_CONFIG, ...config };
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// Set storage adapter
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this.storage = storage;
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// Initialize product quantizer if enabled
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if (this.optimizedConfig.productQuantization?.enabled) {
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this.useProductQuantization = true;
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this.productQuantizer = new ProductQuantizer(this.optimizedConfig.productQuantization.numSubvectors, this.optimizedConfig.productQuantization.numCentroids);
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}
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// Set disk-based index flag
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this.useDiskBasedIndex = this.optimizedConfig.useDiskBasedIndex || false;
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}
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/**
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* Thread-safe method to update memory usage
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* @param memoryDelta Change in memory usage (can be negative)
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* @param vectorCountDelta Change in vector count (can be negative)
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*/
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async updateMemoryUsage(memoryDelta, vectorCountDelta) {
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this.memoryUpdateLock = this.memoryUpdateLock.then(async () => {
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this.memoryUsage = Math.max(0, this.memoryUsage + memoryDelta);
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this.vectorCount = Math.max(0, this.vectorCount + vectorCountDelta);
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});
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await this.memoryUpdateLock;
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}
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/**
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* Thread-safe method to get current memory usage
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* @returns Current memory usage and vector count
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*/
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async getMemoryUsageAsync() {
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await this.memoryUpdateLock;
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return {
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memoryUsage: this.memoryUsage,
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vectorCount: this.vectorCount
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};
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}
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/**
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* Add a vector to the index
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* Uses product quantization if enabled and memory threshold is exceeded
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*/
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async addItem(item) {
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// Check if item is defined
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if (!item) {
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throw new Error('Item is undefined or null');
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}
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const { id, vector } = item;
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// Check if vector is defined
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if (!vector) {
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throw new Error('Vector is undefined or null');
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}
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// Estimate memory usage for this vector
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const vectorMemory = vector.length * 8; // 8 bytes per number (Float64)
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const connectionsMemory = this.optimizedConfig.M * this.optimizedConfig.ml * 16; // Estimate for connections
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const totalMemory = vectorMemory + connectionsMemory;
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// Update memory usage estimate (thread-safe)
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await this.updateMemoryUsage(totalMemory, 1);
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// Check if we should switch to product quantization
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const currentMemoryUsage = await this.getMemoryUsageAsync();
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if (this.useProductQuantization &&
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currentMemoryUsage.memoryUsage > this.optimizedConfig.memoryThreshold &&
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this.productQuantizer &&
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!this.productQuantizer.getDimension()) {
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// Initialize product quantizer with existing vectors
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this.initializeProductQuantizer();
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}
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// If product quantization is active, quantize the vector
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if (this.useProductQuantization &&
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this.productQuantizer &&
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this.productQuantizer.getDimension() > 0) {
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// Quantize the vector
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const codes = this.productQuantizer.quantize(vector);
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// Store the quantized vector
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this.quantizedVectors.set(id, codes);
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// Reconstruct the vector for indexing
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const reconstructedVector = this.productQuantizer.reconstruct(codes);
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// Add the reconstructed vector to the index
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return await super.addItem({ id, vector: reconstructedVector });
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}
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// If disk-based index is active and storage is available, store the vector
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if (this.useDiskBasedIndex && this.storage) {
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// Create a noun object
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const noun = {
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id,
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vector,
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connections: new Map(),
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level: 0
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};
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// Store the noun
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this.storage.saveNoun(noun).catch((error) => {
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console.error(`Failed to save noun ${id} to storage:`, error);
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});
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}
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// Add the vector to the in-memory index
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return await super.addItem(item);
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}
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/**
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* Search for nearest neighbors
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* Uses product quantization if enabled
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*/
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async search(queryVector, k = 10) {
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// Check if query vector is defined
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if (!queryVector) {
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throw new Error('Query vector is undefined or null');
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}
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// If product quantization is active, quantize the query vector
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if (this.useProductQuantization &&
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this.productQuantizer &&
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this.productQuantizer.getDimension() > 0) {
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// Quantize the query vector
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const codes = this.productQuantizer.quantize(queryVector);
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// Reconstruct the query vector
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const reconstructedVector = this.productQuantizer.reconstruct(codes);
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// Search with the reconstructed vector
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return await super.search(reconstructedVector, k);
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}
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// Otherwise, use the standard search
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return await super.search(queryVector, k);
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}
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/**
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* Remove an item from the index
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*/
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removeItem(id) {
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// If product quantization is active, remove the quantized vector
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if (this.useProductQuantization) {
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this.quantizedVectors.delete(id);
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}
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// If disk-based index is active and storage is available, remove the vector from storage
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if (this.useDiskBasedIndex && this.storage) {
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this.storage.deleteNoun(id).catch((error) => {
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console.error(`Failed to delete noun ${id} from storage:`, error);
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});
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}
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// Update memory usage estimate (async operation, but don't block removal)
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this.getMemoryUsageAsync().then((currentMemoryUsage) => {
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if (currentMemoryUsage.vectorCount > 0) {
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const memoryPerVector = currentMemoryUsage.memoryUsage / currentMemoryUsage.vectorCount;
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this.updateMemoryUsage(-memoryPerVector, -1);
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}
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}).catch((error) => {
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console.error('Failed to update memory usage after removal:', error);
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});
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// Remove the item from the in-memory index
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return super.removeItem(id);
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}
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/**
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* Clear the index
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*/
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async clear() {
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// Clear product quantization data
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if (this.useProductQuantization) {
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this.quantizedVectors.clear();
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this.productQuantizer = new ProductQuantizer(this.optimizedConfig.productQuantization.numSubvectors, this.optimizedConfig.productQuantization.numCentroids);
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}
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// Reset memory usage (thread-safe)
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const currentMemoryUsage = await this.getMemoryUsageAsync();
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await this.updateMemoryUsage(-currentMemoryUsage.memoryUsage, -currentMemoryUsage.vectorCount);
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// Clear the in-memory index
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super.clear();
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}
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/**
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* Initialize product quantizer with existing vectors
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*/
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initializeProductQuantizer() {
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if (!this.productQuantizer) {
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return;
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}
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// Get all vectors from the index
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const nouns = super.getNouns();
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const vectors = [];
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// Extract vectors
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for (const [_, noun] of nouns) {
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vectors.push(noun.vector);
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}
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// Train the product quantizer
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if (vectors.length > 0) {
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this.productQuantizer.train(vectors);
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// Quantize all existing vectors
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for (const [id, noun] of nouns) {
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const codes = this.productQuantizer.quantize(noun.vector);
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this.quantizedVectors.set(id, codes);
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}
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console.log(`Initialized product quantizer with ${vectors.length} vectors`);
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}
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}
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/**
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* Get the product quantizer
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* @returns Product quantizer or null if not enabled
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*/
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getProductQuantizer() {
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return this.productQuantizer;
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}
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/**
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* Get the optimized configuration
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* @returns Optimized configuration
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*/
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getOptimizedConfig() {
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return { ...this.optimizedConfig };
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}
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/**
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* Get the estimated memory usage
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* @returns Estimated memory usage in bytes
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*/
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getMemoryUsage() {
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return this.memoryUsage;
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}
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/**
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* Set the storage adapter
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* @param storage Storage adapter
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*/
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setStorage(storage) {
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this.storage = storage;
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}
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/**
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* Get the storage adapter
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* @returns Storage adapter or null if not set
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*/
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getStorage() {
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return this.storage;
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}
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/**
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* Set whether to use disk-based index
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* @param useDiskBasedIndex Whether to use disk-based index
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*/
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setUseDiskBasedIndex(useDiskBasedIndex) {
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this.useDiskBasedIndex = useDiskBasedIndex;
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}
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/**
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* Get whether disk-based index is used
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* @returns Whether disk-based index is used
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*/
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getUseDiskBasedIndex() {
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return this.useDiskBasedIndex;
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}
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/**
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* Set whether to use product quantization
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* @param useProductQuantization Whether to use product quantization
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*/
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setUseProductQuantization(useProductQuantization) {
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this.useProductQuantization = useProductQuantization;
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}
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/**
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* Get whether product quantization is used
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* @returns Whether product quantization is used
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*/
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getUseProductQuantization() {
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return this.useProductQuantization;
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}
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}
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//# sourceMappingURL=hnswIndexOptimized.js.map
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