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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621
dist/hnsw/hnswIndex.js
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621
dist/hnsw/hnswIndex.js
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/**
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* HNSW (Hierarchical Navigable Small World) Index implementation
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* Based on the paper: "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs"
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*/
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import { euclideanDistance, calculateDistancesBatch } from '../utils/index.js';
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// Default HNSW parameters
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const DEFAULT_CONFIG = {
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M: 16, // Max number of connections per noun
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efConstruction: 200, // Size of a dynamic candidate list during construction
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efSearch: 50, // Size of a dynamic candidate list during search
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ml: 16 // Max level
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};
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export class HNSWIndex {
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constructor(config = {}, distanceFunction = euclideanDistance, options = {}) {
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this.nouns = new Map();
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this.entryPointId = null;
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this.maxLevel = 0;
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this.dimension = null;
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this.useParallelization = true; // Whether to use parallelization for performance-critical operations
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this.config = { ...DEFAULT_CONFIG, ...config };
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this.distanceFunction = distanceFunction;
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this.useParallelization =
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options.useParallelization !== undefined
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? options.useParallelization
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: true;
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}
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/**
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* Set whether to use parallelization for performance-critical operations
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*/
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setUseParallelization(useParallelization) {
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this.useParallelization = useParallelization;
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}
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/**
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* Get whether parallelization is enabled
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*/
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getUseParallelization() {
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return this.useParallelization;
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}
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/**
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* Calculate distances between a query vector and multiple vectors in parallel
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* This is used to optimize performance for search operations
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* Uses optimized batch processing for optimal performance
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*
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* @param queryVector The query vector
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* @param vectors Array of vectors to compare against
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* @returns Array of distances
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*/
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async calculateDistancesInParallel(queryVector, vectors) {
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// If parallelization is disabled or there are very few vectors, use sequential processing
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if (!this.useParallelization || vectors.length < 10) {
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return vectors.map((item) => ({
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id: item.id,
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distance: this.distanceFunction(queryVector, item.vector)
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}));
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}
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try {
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// Extract just the vectors from the input array
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const vectorsOnly = vectors.map((item) => item.vector);
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// Use optimized batch distance calculation
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const distances = await calculateDistancesBatch(queryVector, vectorsOnly, this.distanceFunction);
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// Map the distances back to their IDs
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return vectors.map((item, index) => ({
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id: item.id,
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distance: distances[index]
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}));
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}
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catch (error) {
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console.error('Error in batch distance calculation, falling back to sequential processing:', error);
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// Fall back to sequential processing if batch calculation fails
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return vectors.map((item) => ({
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id: item.id,
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distance: this.distanceFunction(queryVector, item.vector)
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}));
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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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*/
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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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// Set dimension on first insert
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if (this.dimension === null) {
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this.dimension = vector.length;
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}
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else 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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// Generate random level for this noun
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const nounLevel = this.getRandomLevel();
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// Create new noun
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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: nounLevel
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};
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// Initialize empty connection sets for each level
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for (let level = 0; level <= nounLevel; level++) {
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noun.connections.set(level, new Set());
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}
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// If this is the first noun, make it the entry point
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if (this.nouns.size === 0) {
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this.entryPointId = id;
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this.maxLevel = nounLevel;
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this.nouns.set(id, noun);
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return id;
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}
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// Find entry point
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if (!this.entryPointId) {
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console.error('Entry point ID is null');
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// If there's no entry point, this is the first noun, so we should have returned earlier
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// This is a safety check
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this.entryPointId = id;
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this.maxLevel = nounLevel;
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this.nouns.set(id, noun);
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return id;
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}
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const entryPoint = this.nouns.get(this.entryPointId);
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if (!entryPoint) {
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console.error(`Entry point with ID ${this.entryPointId} not found`);
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// If the entry point doesn't exist, treat this as the first noun
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this.entryPointId = id;
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this.maxLevel = nounLevel;
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this.nouns.set(id, noun);
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return id;
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}
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let currObj = entryPoint;
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let currDist = this.distanceFunction(vector, entryPoint.vector);
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// Traverse the graph from top to bottom to find the closest noun
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for (let level = this.maxLevel; level > nounLevel; level--) {
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let changed = true;
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while (changed) {
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changed = false;
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// Check all neighbors at current level
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const connections = currObj.connections.get(level) || new Set();
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for (const neighborId of connections) {
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const neighbor = this.nouns.get(neighborId);
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if (!neighbor) {
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// Skip neighbors that don't exist (expected during rapid additions/deletions)
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continue;
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}
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const distToNeighbor = this.distanceFunction(vector, neighbor.vector);
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if (distToNeighbor < currDist) {
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currDist = distToNeighbor;
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currObj = neighbor;
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changed = true;
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}
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}
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}
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}
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// For each level from nounLevel down to 0
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for (let level = Math.min(nounLevel, this.maxLevel); level >= 0; level--) {
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// Find ef nearest elements using greedy search
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const nearestNouns = await this.searchLayer(vector, currObj, this.config.efConstruction, level);
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// Select M nearest neighbors
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const neighbors = this.selectNeighbors(vector, nearestNouns, this.config.M);
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// Add bidirectional connections
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for (const [neighborId, _] of neighbors) {
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const neighbor = this.nouns.get(neighborId);
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if (!neighbor) {
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// Skip neighbors that don't exist (expected during rapid additions/deletions)
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continue;
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}
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noun.connections.get(level).add(neighborId);
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// Add reverse connection
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if (!neighbor.connections.has(level)) {
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neighbor.connections.set(level, new Set());
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}
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neighbor.connections.get(level).add(id);
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// Ensure neighbor doesn't have too many connections
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if (neighbor.connections.get(level).size > this.config.M) {
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this.pruneConnections(neighbor, level);
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}
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}
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// Update entry point for the next level
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if (nearestNouns.size > 0) {
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const [nearestId, nearestDist] = [...nearestNouns][0];
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if (nearestDist < currDist) {
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currDist = nearestDist;
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const nearestNoun = this.nouns.get(nearestId);
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if (!nearestNoun) {
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console.error(`Nearest noun with ID ${nearestId} not found in addItem`);
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// Keep the current object as is
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}
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else {
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currObj = nearestNoun;
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}
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}
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}
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}
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// Update max level and entry point if needed
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if (nounLevel > this.maxLevel) {
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this.maxLevel = nounLevel;
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this.entryPointId = id;
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}
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// Add noun to the index
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this.nouns.set(id, noun);
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return id;
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}
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/**
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* Search for nearest neighbors
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*/
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async search(queryVector, k = 10, filter) {
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if (this.nouns.size === 0) {
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return [];
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}
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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 (this.dimension !== null && queryVector.length !== this.dimension) {
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throw new Error(`Query vector dimension mismatch: expected ${this.dimension}, got ${queryVector.length}`);
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}
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// Start from the entry point
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if (!this.entryPointId) {
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console.error('Entry point ID is null');
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return [];
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}
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const entryPoint = this.nouns.get(this.entryPointId);
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if (!entryPoint) {
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console.error(`Entry point with ID ${this.entryPointId} not found`);
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return [];
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}
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let currObj = entryPoint;
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let currDist = this.distanceFunction(queryVector, currObj.vector);
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// Traverse the graph from top to bottom to find the closest noun
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for (let level = this.maxLevel; level > 0; level--) {
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let changed = true;
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while (changed) {
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changed = false;
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// Check all neighbors at current level
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const connections = currObj.connections.get(level) || new Set();
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// If we have enough connections, use parallel distance calculation
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if (this.useParallelization && connections.size >= 10) {
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// Prepare vectors for parallel calculation
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const vectors = [];
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for (const neighborId of connections) {
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const neighbor = this.nouns.get(neighborId);
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if (!neighbor)
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continue;
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vectors.push({ id: neighborId, vector: neighbor.vector });
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}
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// Calculate distances in parallel
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const distances = await this.calculateDistancesInParallel(queryVector, vectors);
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// Find the closest neighbor
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for (const { id, distance } of distances) {
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if (distance < currDist) {
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currDist = distance;
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const neighbor = this.nouns.get(id);
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if (neighbor) {
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currObj = neighbor;
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changed = true;
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}
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}
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}
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}
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else {
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// Use sequential processing for small number of connections
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for (const neighborId of connections) {
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const neighbor = this.nouns.get(neighborId);
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if (!neighbor) {
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// Skip neighbors that don't exist (expected during rapid additions/deletions)
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continue;
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}
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const distToNeighbor = this.distanceFunction(queryVector, neighbor.vector);
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if (distToNeighbor < currDist) {
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currDist = distToNeighbor;
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currObj = neighbor;
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changed = true;
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}
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}
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}
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}
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}
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// Search at level 0 with ef = k
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// If we have a filter, increase ef to compensate for filtered results
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const ef = filter ? Math.max(this.config.efSearch * 3, k * 3) : Math.max(this.config.efSearch, k);
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const nearestNouns = await this.searchLayer(queryVector, currObj, ef, 0, filter);
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// Convert to array and sort by distance
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return [...nearestNouns].slice(0, 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 (!this.nouns.has(id)) {
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return false;
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}
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const noun = this.nouns.get(id);
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// Remove connections to this noun from all neighbors
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for (const [level, connections] of noun.connections.entries()) {
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for (const neighborId of connections) {
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const neighbor = this.nouns.get(neighborId);
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if (!neighbor) {
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// Skip neighbors that don't exist (expected during rapid additions/deletions)
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continue;
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}
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if (neighbor.connections.has(level)) {
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neighbor.connections.get(level).delete(id);
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// Prune connections after removing this noun to ensure consistency
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this.pruneConnections(neighbor, level);
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}
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}
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}
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// Also check all other nouns for references to this noun and remove them
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for (const [nounId, otherNoun] of this.nouns.entries()) {
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if (nounId === id)
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continue; // Skip the noun being removed
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for (const [level, connections] of otherNoun.connections.entries()) {
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if (connections.has(id)) {
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connections.delete(id);
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// Prune connections after removing this reference
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this.pruneConnections(otherNoun, level);
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}
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}
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}
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// Remove the noun
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this.nouns.delete(id);
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// If we removed the entry point, find a new one
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if (this.entryPointId === id) {
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if (this.nouns.size === 0) {
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this.entryPointId = null;
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this.maxLevel = 0;
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}
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else {
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// Find the noun with the highest level
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let maxLevel = 0;
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let newEntryPointId = null;
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for (const [nounId, noun] of this.nouns.entries()) {
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if (noun.connections.size === 0)
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continue; // Skip nouns with no connections
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const nounLevel = Math.max(...noun.connections.keys());
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if (nounLevel >= maxLevel) {
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maxLevel = nounLevel;
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newEntryPointId = nounId;
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}
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}
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this.entryPointId = newEntryPointId;
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this.maxLevel = maxLevel;
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}
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}
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return true;
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}
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/**
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* Get all nouns in the index
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* @deprecated Use getNounsPaginated() instead for better scalability
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*/
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getNouns() {
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return new Map(this.nouns);
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}
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/**
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* Get nouns with pagination
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* @param options Pagination options
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* @returns Object containing paginated nouns and pagination info
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*/
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getNounsPaginated(options = {}) {
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const offset = options.offset || 0;
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const limit = options.limit || 100;
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const filter = options.filter || (() => true);
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// Get all noun entries
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const entries = [...this.nouns.entries()];
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// Apply filter if provided
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const filteredEntries = entries.filter(([_, noun]) => filter(noun));
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// Get total count after filtering
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const totalCount = filteredEntries.length;
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// Apply pagination
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const paginatedEntries = filteredEntries.slice(offset, offset + limit);
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// Check if there are more items
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const hasMore = offset + limit < totalCount;
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// Create a new map with the paginated entries
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const items = new Map(paginatedEntries);
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return {
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items,
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totalCount,
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hasMore
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};
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}
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/**
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* Clear the index
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*/
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clear() {
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this.nouns.clear();
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this.entryPointId = null;
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this.maxLevel = 0;
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}
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/**
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* Get the size of the index
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*/
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size() {
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return this.nouns.size;
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}
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/**
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* Get the distance function used by the index
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*/
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getDistanceFunction() {
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return this.distanceFunction;
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}
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/**
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* Get the entry point ID
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*/
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getEntryPointId() {
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return this.entryPointId;
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}
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/**
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* Get the maximum level
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*/
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getMaxLevel() {
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return this.maxLevel;
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}
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||||
/**
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* Get the 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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/**
|
||||
* Get the configuration
|
||||
*/
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getConfig() {
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return { ...this.config };
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||||
}
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/**
|
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* Get index health metrics
|
||||
*/
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getIndexHealth() {
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let totalConnections = 0;
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const layerCounts = new Array(this.maxLevel + 1).fill(0);
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// Count connections and layer distribution
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||||
this.nouns.forEach(noun => {
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// Count connections at each layer
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for (let level = 0; level <= noun.level; level++) {
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totalConnections += noun.connections.get(level)?.size || 0;
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layerCounts[level]++;
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}
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});
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const totalNodes = this.nouns.size;
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const averageConnections = totalNodes > 0 ? totalConnections / totalNodes : 0;
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return {
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averageConnections,
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layerDistribution: layerCounts,
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maxLayer: this.maxLevel,
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totalNodes
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};
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||||
}
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||||
/**
|
||||
* Search within a specific layer
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||||
* Returns a map of noun IDs to distances, sorted by distance
|
||||
*/
|
||||
async searchLayer(queryVector, entryPoint, ef, level, filter) {
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||||
// Set of visited nouns
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||||
const visited = new Set([entryPoint.id]);
|
||||
// Check if entry point passes filter
|
||||
const entryPointDistance = this.distanceFunction(queryVector, entryPoint.vector);
|
||||
const entryPointPasses = filter ? await filter(entryPoint.id) : true;
|
||||
// Priority queue of candidates (closest first)
|
||||
const candidates = new Map();
|
||||
candidates.set(entryPoint.id, entryPointDistance);
|
||||
// Priority queue of nearest neighbors found so far (closest first)
|
||||
const nearest = new Map();
|
||||
if (entryPointPasses) {
|
||||
nearest.set(entryPoint.id, entryPointDistance);
|
||||
}
|
||||
// While there are candidates to explore
|
||||
while (candidates.size > 0) {
|
||||
// Get closest candidate
|
||||
const [closestId, closestDist] = [...candidates][0];
|
||||
candidates.delete(closestId);
|
||||
// If this candidate is farther than the farthest in our result set, we're done
|
||||
const farthestInNearest = [...nearest][nearest.size - 1];
|
||||
if (nearest.size >= ef && closestDist > farthestInNearest[1]) {
|
||||
break;
|
||||
}
|
||||
// Explore neighbors of the closest candidate
|
||||
const noun = this.nouns.get(closestId);
|
||||
if (!noun) {
|
||||
console.error(`Noun with ID ${closestId} not found in searchLayer`);
|
||||
continue;
|
||||
}
|
||||
const connections = noun.connections.get(level) || new Set();
|
||||
// If we have enough connections and parallelization is enabled, use parallel distance calculation
|
||||
if (this.useParallelization && connections.size >= 10) {
|
||||
// Collect unvisited neighbors
|
||||
const unvisitedNeighbors = [];
|
||||
for (const neighborId of connections) {
|
||||
if (!visited.has(neighborId)) {
|
||||
visited.add(neighborId);
|
||||
const neighbor = this.nouns.get(neighborId);
|
||||
if (!neighbor)
|
||||
continue;
|
||||
unvisitedNeighbors.push({ id: neighborId, vector: neighbor.vector });
|
||||
}
|
||||
}
|
||||
if (unvisitedNeighbors.length > 0) {
|
||||
// Calculate distances in parallel
|
||||
const distances = await this.calculateDistancesInParallel(queryVector, unvisitedNeighbors);
|
||||
// Process the results
|
||||
for (const { id, distance } of distances) {
|
||||
// Apply filter if provided
|
||||
const passes = filter ? await filter(id) : true;
|
||||
// Always add to candidates for graph traversal
|
||||
candidates.set(id, distance);
|
||||
// Only add to nearest if it passes the filter
|
||||
if (passes) {
|
||||
// If we haven't found ef nearest neighbors yet, or this neighbor is closer than the farthest one we've found
|
||||
if (nearest.size < ef || distance < farthestInNearest[1]) {
|
||||
nearest.set(id, distance);
|
||||
// If we have more than ef neighbors, remove the farthest one
|
||||
if (nearest.size > ef) {
|
||||
const sortedNearest = [...nearest].sort((a, b) => a[1] - b[1]);
|
||||
nearest.clear();
|
||||
for (let i = 0; i < ef; i++) {
|
||||
nearest.set(sortedNearest[i][0], sortedNearest[i][1]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
else {
|
||||
// Use sequential processing for small number of connections
|
||||
for (const neighborId of connections) {
|
||||
if (!visited.has(neighborId)) {
|
||||
visited.add(neighborId);
|
||||
const neighbor = this.nouns.get(neighborId);
|
||||
if (!neighbor) {
|
||||
// Skip neighbors that don't exist (expected during rapid additions/deletions)
|
||||
continue;
|
||||
}
|
||||
const distToNeighbor = this.distanceFunction(queryVector, neighbor.vector);
|
||||
// Apply filter if provided
|
||||
const passes = filter ? await filter(neighborId) : true;
|
||||
// Always add to candidates for graph traversal
|
||||
candidates.set(neighborId, distToNeighbor);
|
||||
// Only add to nearest if it passes the filter
|
||||
if (passes) {
|
||||
// If we haven't found ef nearest neighbors yet, or this neighbor is closer than the farthest one we've found
|
||||
if (nearest.size < ef || distToNeighbor < farthestInNearest[1]) {
|
||||
nearest.set(neighborId, distToNeighbor);
|
||||
// If we have more than ef neighbors, remove the farthest one
|
||||
if (nearest.size > ef) {
|
||||
const sortedNearest = [...nearest].sort((a, b) => a[1] - b[1]);
|
||||
nearest.clear();
|
||||
for (let i = 0; i < ef; i++) {
|
||||
nearest.set(sortedNearest[i][0], sortedNearest[i][1]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
// Sort nearest by distance
|
||||
return new Map([...nearest].sort((a, b) => a[1] - b[1]));
|
||||
}
|
||||
/**
|
||||
* Select M nearest neighbors from the candidate set
|
||||
*/
|
||||
selectNeighbors(queryVector, candidates, M) {
|
||||
if (candidates.size <= M) {
|
||||
return candidates;
|
||||
}
|
||||
// Simple heuristic: just take the M closest
|
||||
const sortedCandidates = [...candidates].sort((a, b) => a[1] - b[1]);
|
||||
const result = new Map();
|
||||
for (let i = 0; i < Math.min(M, sortedCandidates.length); i++) {
|
||||
result.set(sortedCandidates[i][0], sortedCandidates[i][1]);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
/**
|
||||
* Ensure a noun doesn't have too many connections at a given level
|
||||
*/
|
||||
pruneConnections(noun, level) {
|
||||
const connections = noun.connections.get(level);
|
||||
if (connections.size <= this.config.M) {
|
||||
return;
|
||||
}
|
||||
// Calculate distances to all neighbors
|
||||
const distances = new Map();
|
||||
const validNeighborIds = new Set();
|
||||
for (const neighborId of connections) {
|
||||
const neighbor = this.nouns.get(neighborId);
|
||||
if (!neighbor) {
|
||||
// Skip neighbors that don't exist (expected during rapid additions/deletions)
|
||||
continue;
|
||||
}
|
||||
// Only add valid neighbors to the distances map
|
||||
distances.set(neighborId, this.distanceFunction(noun.vector, neighbor.vector));
|
||||
validNeighborIds.add(neighborId);
|
||||
}
|
||||
// Only proceed if we have valid neighbors
|
||||
if (distances.size === 0) {
|
||||
// If no valid neighbors, clear connections at this level
|
||||
noun.connections.set(level, new Set());
|
||||
return;
|
||||
}
|
||||
// Select M closest neighbors from valid ones
|
||||
const selectedNeighbors = this.selectNeighbors(noun.vector, distances, this.config.M);
|
||||
// Update connections with only valid neighbors
|
||||
noun.connections.set(level, new Set(selectedNeighbors.keys()));
|
||||
}
|
||||
/**
|
||||
* Generate a random level for a new noun
|
||||
* Uses the same distribution as in the original HNSW paper
|
||||
*/
|
||||
getRandomLevel() {
|
||||
const r = Math.random();
|
||||
return Math.floor(-Math.log(r) * (1.0 / Math.log(this.config.M)));
|
||||
}
|
||||
}
|
||||
//# sourceMappingURL=hnswIndex.js.map
|
||||
Loading…
Add table
Add a link
Reference in a new issue