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/partitionedHNSWIndex.js
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dist/hnsw/partitionedHNSWIndex.js
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/**
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* Partitioned HNSW Index for Large-Scale Vector Search
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* Implements sharding strategies to handle millions of vectors efficiently
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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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* Partitioned HNSW Index that splits large datasets across multiple smaller indices
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* This enables efficient search across millions of vectors by reducing memory usage
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* and parallelizing search operations
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*/
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export class PartitionedHNSWIndex {
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constructor(partitionConfig = {}, hnswConfig = {}, distanceFunction = euclideanDistance) {
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this.partitions = new Map();
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this.partitionMetadata = new Map();
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this.dimension = null;
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this.nextPartitionId = 0;
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this.config = {
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maxNodesPerPartition: 50000, // Optimal size for memory efficiency
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partitionStrategy: 'semantic', // Default to semantic for better performance
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semanticClusters: 8, // Auto-tuned based on dataset
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autoTuneSemanticClusters: true,
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...partitionConfig
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};
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// Optimized HNSW parameters for large scale
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this.hnswConfig = {
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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, // Balance speed vs accuracy
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ml: 24, // Deeper hierarchy
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...hnswConfig
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};
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this.distanceFunction = distanceFunction;
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}
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/**
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* Add a vector to the partitioned index
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*/
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async addItem(item) {
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if (this.dimension === null) {
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this.dimension = item.vector.length;
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}
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// Determine which partition this item belongs to
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const partitionId = await this.selectPartition(item);
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// Get or create the partition
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let partition = this.partitions.get(partitionId);
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if (!partition) {
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partition = new HNSWIndex(this.hnswConfig, this.distanceFunction, { useParallelization: true });
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this.partitions.set(partitionId, partition);
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// Initialize partition metadata
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this.partitionMetadata.set(partitionId, {
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id: partitionId,
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nodeCount: 0,
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strategy: this.config.partitionStrategy,
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created: new Date()
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});
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}
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// Add item to the selected partition
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await partition.addItem(item);
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// Update partition metadata
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const metadata = this.partitionMetadata.get(partitionId);
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metadata.nodeCount = partition.size();
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// Update bounds for semantic strategy
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if (this.config.partitionStrategy === 'semantic') {
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this.updatePartitionBounds(partitionId, item.vector);
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}
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// Check if partition is getting too large and needs splitting
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if (metadata.nodeCount > this.config.maxNodesPerPartition * 1.2) {
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await this.splitPartition(partitionId);
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}
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return item.id;
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}
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/**
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* Search across all partitions for nearest neighbors
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*/
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async search(queryVector, k = 10, searchScope) {
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if (this.partitions.size === 0) {
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return [];
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}
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// Determine which partitions to search
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const partitionsToSearch = await this.selectSearchPartitions(queryVector, searchScope);
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// Search partitions in parallel
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const searchPromises = partitionsToSearch.map(async (partitionId) => {
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const partition = this.partitions.get(partitionId);
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if (!partition)
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return [];
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// Search with higher k to get better global results
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const partitionK = Math.min(k * 2, partition.size());
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return partition.search(queryVector, partitionK);
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});
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const partitionResults = await Promise.all(searchPromises);
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// Merge and sort results from all partitions
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const allResults = [];
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for (const results of partitionResults) {
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allResults.push(...results);
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}
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// Sort by distance and return top k
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allResults.sort((a, b) => a[1] - b[1]);
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return allResults.slice(0, k);
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}
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/**
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* Select the appropriate partition for a new item
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* Automatically chooses semantic partitioning when beneficial, falls back to hash
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*/
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async selectPartition(item) {
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// Auto-tune semantic clusters based on current dataset size
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if (this.config.autoTuneSemanticClusters && this.config.partitionStrategy === 'semantic') {
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this.autoTuneSemanticClusters();
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}
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switch (this.config.partitionStrategy) {
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case 'semantic':
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return await this.semanticPartition(item.vector);
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case 'hash':
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default:
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return this.hashPartition(item.id);
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}
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}
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/**
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* Hash-based partitioning for even distribution
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*/
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hashPartition(id) {
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const hash = this.simpleHash(id);
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const existingPartitions = Array.from(this.partitions.keys());
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// Find partition with space, or create new one
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for (const partitionId of existingPartitions) {
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const metadata = this.partitionMetadata.get(partitionId);
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if (metadata && metadata.nodeCount < this.config.maxNodesPerPartition) {
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return partitionId;
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}
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}
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// Create new partition
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return `partition_${this.nextPartitionId++}`;
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}
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/**
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* Semantic clustering partitioning
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*/
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async semanticPartition(vector) {
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// Find closest partition centroid
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let closestPartition = '';
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let minDistance = Infinity;
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for (const [partitionId, metadata] of this.partitionMetadata.entries()) {
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if (metadata.bounds?.centroid) {
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const distance = this.distanceFunction(vector, metadata.bounds.centroid);
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if (distance < minDistance) {
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minDistance = distance;
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closestPartition = partitionId;
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}
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}
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}
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// If no suitable partition found or it's full, create new one
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if (!closestPartition ||
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this.partitionMetadata.get(closestPartition).nodeCount >= this.config.maxNodesPerPartition) {
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closestPartition = `semantic_${this.nextPartitionId++}`;
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}
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return closestPartition;
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}
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/**
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* Auto-tune semantic clusters based on dataset size and performance
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*/
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autoTuneSemanticClusters() {
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const totalNodes = this.size();
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const currentPartitions = this.partitions.size;
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// Optimal clusters based on dataset size
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let optimalClusters = Math.max(4, Math.min(32, Math.floor(totalNodes / 10000)));
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// Adjust based on current partition performance
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if (currentPartitions > 0) {
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const avgNodesPerPartition = totalNodes / currentPartitions;
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if (avgNodesPerPartition > this.config.maxNodesPerPartition * 0.8) {
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// Partitions are getting full, increase clusters
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optimalClusters = Math.min(32, this.config.semanticClusters + 2);
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}
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else if (avgNodesPerPartition < this.config.maxNodesPerPartition * 0.3 && currentPartitions > 4) {
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// Partitions are underutilized, decrease clusters
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optimalClusters = Math.max(4, this.config.semanticClusters - 1);
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}
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}
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if (optimalClusters !== this.config.semanticClusters) {
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console.log(`Auto-tuning semantic clusters: ${this.config.semanticClusters} → ${optimalClusters}`);
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this.config.semanticClusters = optimalClusters;
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}
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}
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/**
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* Select which partitions to search based on query
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*/
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async selectSearchPartitions(queryVector, searchScope) {
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if (searchScope?.partitionIds) {
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return searchScope.partitionIds.filter(id => this.partitions.has(id));
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}
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const maxPartitions = searchScope?.maxPartitions || Math.min(5, this.partitions.size);
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if (this.config.partitionStrategy === 'semantic') {
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// Search partitions with closest centroids
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const distances = [];
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for (const [partitionId, metadata] of this.partitionMetadata.entries()) {
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if (metadata.bounds?.centroid) {
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const distance = this.distanceFunction(queryVector, metadata.bounds.centroid);
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distances.push([partitionId, distance]);
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}
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}
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distances.sort((a, b) => a[1] - b[1]);
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return distances.slice(0, maxPartitions).map(([id]) => id);
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}
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// For other strategies, search all partitions or random subset
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const allPartitionIds = Array.from(this.partitions.keys());
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if (allPartitionIds.length <= maxPartitions) {
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return allPartitionIds;
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}
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// Return random subset
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const shuffled = [...allPartitionIds].sort(() => Math.random() - 0.5);
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return shuffled.slice(0, maxPartitions);
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}
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/**
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* Update partition bounds for semantic clustering
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*/
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updatePartitionBounds(partitionId, vector) {
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const metadata = this.partitionMetadata.get(partitionId);
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if (!metadata.bounds) {
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metadata.bounds = {
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centroid: [...vector],
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radius: 0
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};
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return;
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}
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// Update centroid using incremental mean
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const { centroid } = metadata.bounds;
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const nodeCount = metadata.nodeCount;
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for (let i = 0; i < centroid.length; i++) {
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centroid[i] = (centroid[i] * (nodeCount - 1) + vector[i]) / nodeCount;
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}
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// Update radius
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const distance = this.distanceFunction(vector, centroid);
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metadata.bounds.radius = Math.max(metadata.bounds.radius, distance);
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}
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/**
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* Split an overgrown partition into smaller partitions
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*/
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async splitPartition(partitionId) {
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const partition = this.partitions.get(partitionId);
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if (!partition)
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return;
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console.log(`Splitting partition ${partitionId} with ${partition.size()} nodes`);
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// For now, we'll implement a simple strategy
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// In a full implementation, you'd want to analyze the data distribution
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// and create more intelligent splits
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// This is a placeholder - actual implementation would require
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// accessing the internal nodes of the HNSW index
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}
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/**
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* Simple hash function for consistent partitioning
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*/
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simpleHash(str) {
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let hash = 0;
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for (let i = 0; i < str.length; i++) {
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const char = str.charCodeAt(i);
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hash = ((hash << 5) - hash) + char;
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hash = hash & hash; // Convert to 32-bit integer
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}
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return Math.abs(hash);
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}
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/**
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* Get partition statistics
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*/
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getPartitionStats() {
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const partitionDetails = Array.from(this.partitionMetadata.values());
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const totalNodes = partitionDetails.reduce((sum, p) => sum + p.nodeCount, 0);
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return {
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totalPartitions: partitionDetails.length,
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totalNodes,
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averageNodesPerPartition: totalNodes / partitionDetails.length || 0,
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partitionDetails
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};
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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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async removeItem(id) {
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// Find which partition contains this item
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for (const [partitionId, partition] of this.partitions.entries()) {
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if (partition.removeItem(id)) {
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// Update metadata
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const metadata = this.partitionMetadata.get(partitionId);
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metadata.nodeCount = partition.size();
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return true;
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}
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}
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return false;
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}
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/**
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* Clear all partitions
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*/
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clear() {
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for (const partition of this.partitions.values()) {
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partition.clear();
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}
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this.partitions.clear();
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this.partitionMetadata.clear();
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this.nextPartitionId = 0;
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}
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/**
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* Get total size across all partitions
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*/
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size() {
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return Array.from(this.partitions.values()).reduce((sum, partition) => sum + partition.size(), 0);
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}
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}
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//# sourceMappingURL=partitionedHNSWIndex.js.map
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