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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dist/hnsw/optimizedHNSWIndex.js
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313
dist/hnsw/optimizedHNSWIndex.js
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/**
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* Optimized HNSW Index for Large-Scale Vector Search
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* Implements dynamic parameter tuning and performance optimizations
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*/
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import { HNSWIndex } from './hnswIndex.js';
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import { euclideanDistance } from '../utils/index.js';
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/**
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* Optimized HNSW Index with dynamic parameter tuning for large datasets
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*/
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export class OptimizedHNSWIndex extends HNSWIndex {
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constructor(config = {}, distanceFunction = euclideanDistance) {
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// Set optimized defaults for large scale
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const defaultConfig = {
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M: 32, // Higher connectivity for better recall
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efConstruction: 400, // Better build quality
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efSearch: 100, // Dynamic - will be tuned
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ml: 24, // Deeper hierarchy
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useDiskBasedIndex: false, // Added missing property
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dynamicParameterTuning: true,
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targetSearchLatency: 100, // 100ms target
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targetRecall: 0.95, // 95% recall target
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maxNodes: 1000000, // 1M node limit
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memoryBudget: 8 * 1024 * 1024 * 1024, // 8GB
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diskCacheEnabled: true,
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compressionEnabled: false, // Disabled by default for compatibility
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performanceTracking: true,
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adaptiveEfSearch: true,
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levelMultiplier: 16,
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seedConnections: 8,
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pruningStrategy: 'hybrid'
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};
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const mergedConfig = { ...defaultConfig, ...config };
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// Initialize parent with base config
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super({
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M: mergedConfig.M,
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efConstruction: mergedConfig.efConstruction,
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efSearch: mergedConfig.efSearch,
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ml: mergedConfig.ml
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}, distanceFunction, { useParallelization: true });
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this.searchHistory = [];
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this.optimizedConfig = mergedConfig;
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// Initialize dynamic parameters
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this.dynamicParams = {
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efSearch: mergedConfig.efSearch,
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efConstruction: mergedConfig.efConstruction,
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M: mergedConfig.M,
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ml: mergedConfig.ml
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};
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// Initialize performance metrics
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this.performanceMetrics = {
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averageSearchTime: 0,
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averageRecall: 0,
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memoryUsage: 0,
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indexSize: 0,
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apiCalls: 0,
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cacheHitRate: 0
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};
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// Start parameter tuning if enabled
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if (this.optimizedConfig.dynamicParameterTuning) {
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this.startParameterTuning();
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}
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}
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/**
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* Optimized search with dynamic parameter adjustment
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*/
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async search(queryVector, k = 10, filter) {
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const startTime = Date.now();
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// Adjust efSearch dynamically based on k and performance history
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if (this.optimizedConfig.adaptiveEfSearch) {
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this.adjustEfSearch(k);
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}
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// Check memory usage and trigger optimizations if needed
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if (this.optimizedConfig.performanceTracking) {
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this.checkMemoryUsage();
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}
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// Perform the search with current parameters
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const originalConfig = this.getConfig();
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// Temporarily update search parameters
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const tempConfig = {
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...originalConfig,
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efSearch: this.dynamicParams.efSearch
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};
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// Use the parent's search method with optimized parameters
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let results;
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try {
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// This is a simplified approach - in practice, we'd need to modify
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// the parent class to accept runtime parameter changes
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results = await super.search(queryVector, k, filter);
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}
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catch (error) {
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console.error('Optimized search failed, falling back to default:', error);
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results = await super.search(queryVector, k, filter);
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}
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// Record performance metrics
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const searchTime = Date.now() - startTime;
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this.recordSearchMetrics(searchTime, k, results.length);
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return results;
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}
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/**
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* Dynamically adjust efSearch based on performance requirements
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*/
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adjustEfSearch(k) {
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const recentSearches = this.searchHistory.slice(-10);
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if (recentSearches.length < 3) {
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// Not enough data, use heuristic
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this.dynamicParams.efSearch = Math.max(k * 2, 50);
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return;
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}
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const averageLatency = recentSearches.reduce((sum, s) => sum + s.latency, 0) / recentSearches.length;
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const targetLatency = this.optimizedConfig.targetSearchLatency;
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// Adjust efSearch based on latency performance
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if (averageLatency > targetLatency * 1.2) {
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// Too slow, reduce efSearch
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this.dynamicParams.efSearch = Math.max(Math.floor(this.dynamicParams.efSearch * 0.9), k);
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}
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else if (averageLatency < targetLatency * 0.8) {
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// Fast enough, can increase efSearch for better recall
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this.dynamicParams.efSearch = Math.min(Math.floor(this.dynamicParams.efSearch * 1.1), 500 // Maximum efSearch
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);
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}
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// Ensure efSearch is at least k
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this.dynamicParams.efSearch = Math.max(this.dynamicParams.efSearch, k);
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}
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/**
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* Record search performance metrics
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*/
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recordSearchMetrics(latency, k, resultCount) {
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if (!this.optimizedConfig.performanceTracking) {
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return;
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}
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// Add to search history
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this.searchHistory.push({
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latency,
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k,
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timestamp: Date.now()
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});
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// Keep only recent history (last 100 searches)
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if (this.searchHistory.length > 100) {
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this.searchHistory.shift();
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}
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// Update performance metrics
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const recentSearches = this.searchHistory.slice(-20);
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this.performanceMetrics.averageSearchTime =
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recentSearches.reduce((sum, s) => sum + s.latency, 0) / recentSearches.length;
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// Estimate recall (simplified - would need ground truth for accurate measurement)
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this.performanceMetrics.averageRecall = Math.min(resultCount / k, 1.0);
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}
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/**
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* Check memory usage and trigger optimizations
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*/
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checkMemoryUsage() {
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// Estimate memory usage (simplified)
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const estimatedMemory = this.size() * 1000; // Rough estimate per node
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this.performanceMetrics.memoryUsage = estimatedMemory;
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if (estimatedMemory > this.optimizedConfig.memoryBudget * 0.9) {
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console.warn('Memory usage approaching limit, consider index partitioning');
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// Could trigger automatic partitioning or compression here
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if (this.optimizedConfig.compressionEnabled) {
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this.compressIndex();
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}
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}
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}
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/**
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* Compress index to reduce memory usage (placeholder)
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*/
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compressIndex() {
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console.log('Index compression not implemented yet');
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// This would implement vector quantization or other compression techniques
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}
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/**
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* Start automatic parameter tuning
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*/
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startParameterTuning() {
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this.parameterTuningInterval = setInterval(() => {
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this.tuneParameters();
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}, 30000); // Tune every 30 seconds
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}
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/**
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* Automatic parameter tuning based on performance metrics
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*/
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tuneParameters() {
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if (this.searchHistory.length < 10) {
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return; // Not enough data
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}
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const recentSearches = this.searchHistory.slice(-20);
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const averageLatency = recentSearches.reduce((sum, s) => sum + s.latency, 0) / recentSearches.length;
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// Tune based on performance vs targets
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const latencyRatio = averageLatency / this.optimizedConfig.targetSearchLatency;
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const recallRatio = this.performanceMetrics.averageRecall / this.optimizedConfig.targetRecall;
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// Adjust M (connectivity) for long-term performance
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if (this.size() > 10000) { // Only tune for larger indices
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if (recallRatio < 0.95 && latencyRatio < 1.5) {
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// Recall is low but we have latency budget, increase M
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this.dynamicParams.M = Math.min(this.dynamicParams.M + 2, 64);
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}
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else if (latencyRatio > 1.2 && recallRatio > 1.0) {
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// Latency is high but recall is good, can reduce M
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this.dynamicParams.M = Math.max(this.dynamicParams.M - 2, 16);
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}
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}
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console.log(`Parameter tuning: efSearch=${this.dynamicParams.efSearch}, M=${this.dynamicParams.M}, latency=${averageLatency.toFixed(1)}ms`);
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}
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/**
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* Get optimized configuration recommendations for current dataset size
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*/
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getOptimizedConfig() {
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const currentSize = this.size();
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let recommendedConfig = {};
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if (currentSize < 10000) {
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// Small dataset - optimize for speed
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recommendedConfig = {
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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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};
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}
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else if (currentSize < 100000) {
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// Medium dataset - balance speed and recall
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recommendedConfig = {
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M: 24,
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efConstruction: 300,
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efSearch: 75,
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ml: 20
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};
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}
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else if (currentSize < 1000000) {
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// Large dataset - optimize for recall
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recommendedConfig = {
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M: 32,
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efConstruction: 400,
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efSearch: 100,
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ml: 24
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};
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}
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else {
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// Very large dataset - maximum quality
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recommendedConfig = {
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M: 48,
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efConstruction: 500,
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efSearch: 150,
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ml: 28
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};
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}
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return {
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...this.optimizedConfig,
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...recommendedConfig
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};
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}
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/**
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* Get current performance metrics
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*/
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getPerformanceMetrics() {
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return {
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...this.performanceMetrics,
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currentParams: { ...this.dynamicParams },
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searchHistorySize: this.searchHistory.length
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};
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}
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/**
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* Apply optimized bulk insertion strategy
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*/
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async bulkInsert(items) {
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console.log(`Starting optimized bulk insert of ${items.length} items`);
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// Sort items to optimize insertion order (by vector similarity)
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const sortedItems = this.optimizeInsertionOrder(items);
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// Temporarily adjust construction parameters for bulk operations
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const originalEfConstruction = this.dynamicParams.efConstruction;
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this.dynamicParams.efConstruction = Math.min(this.dynamicParams.efConstruction * 1.5, 800);
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const results = [];
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const batchSize = 100;
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try {
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// Process in batches to manage memory
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for (let i = 0; i < sortedItems.length; i += batchSize) {
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const batch = sortedItems.slice(i, i + batchSize);
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for (const item of batch) {
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const id = await this.addItem(item);
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results.push(id);
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}
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// Periodic memory check
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if (i % (batchSize * 10) === 0) {
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this.checkMemoryUsage();
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}
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}
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}
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finally {
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// Restore original construction parameters
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this.dynamicParams.efConstruction = originalEfConstruction;
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}
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console.log(`Completed bulk insert of ${results.length} items`);
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return results;
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}
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/**
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* Optimize insertion order to improve index quality
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*/
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optimizeInsertionOrder(items) {
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if (items.length < 100) {
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return items; // Not worth optimizing small batches
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}
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// Simple clustering-based ordering
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// In practice, you might use more sophisticated methods
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return items.sort(() => Math.random() - 0.5); // Shuffle for now
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}
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/**
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* Cleanup resources
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*/
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destroy() {
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if (this.parameterTuningInterval) {
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clearInterval(this.parameterTuningInterval);
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}
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}
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}
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//# sourceMappingURL=optimizedHNSWIndex.js.map
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