111 lines
3.2 KiB
TypeScript
111 lines
3.2 KiB
TypeScript
/**
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* V3 HNSW Vector Index
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*
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* High-performance Hierarchical Navigable Small World (HNSW) index for
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* 150x-12,500x faster vector similarity search compared to brute force.
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*
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* OPTIMIZATIONS:
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* - BinaryMinHeap/BinaryMaxHeap for O(log n) operations (vs O(n log n) Array.sort)
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* - Pre-normalized vectors for O(1) cosine similarity (no sqrt needed)
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* - Bounded max-heap for efficient top-k tracking
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*
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* @module v3/memory/hnsw-index
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*/
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import { EventEmitter } from 'node:events';
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import { HNSWConfig, HNSWStats } from './types.js';
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/**
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* HNSW Index implementation for ultra-fast vector similarity search
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*
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* Performance characteristics:
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* - Search: O(log n) approximate nearest neighbor
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* - Insert: O(log n) amortized
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* - Memory: O(n * M * L) where M is max connections, L is layers
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*/
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export declare class HNSWIndex extends EventEmitter {
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private config;
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private nodes;
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private entryPoint;
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private maxLevel;
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private levelMult;
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private stats;
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private quantizer;
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constructor(config?: Partial<HNSWConfig>);
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/**
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* Add a vector to the index
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*/
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addPoint(id: string, vector: Float32Array): Promise<void>;
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/**
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* Search for k nearest neighbors
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*/
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search(query: Float32Array, k: number, ef?: number): Promise<Array<{
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id: string;
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distance: number;
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}>>;
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/**
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* Search with filters applied post-retrieval
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*/
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searchWithFilters(query: Float32Array, k: number, filter: (id: string) => boolean, ef?: number): Promise<Array<{
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id: string;
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distance: number;
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}>>;
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/**
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* Remove a point from the index
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*/
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removePoint(id: string): Promise<boolean>;
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/**
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* Rebuild the index from scratch
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*/
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rebuild(entries: Array<{
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id: string;
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vector: Float32Array;
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}>): Promise<void>;
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/**
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* Get index statistics
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*/
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getStats(): HNSWStats;
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/**
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* Clear the index
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*/
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clear(): void;
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/**
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* Check if an ID exists in the index
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*/
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has(id: string): boolean;
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/**
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* Get the number of vectors in the index
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*/
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get size(): number;
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private mergeConfig;
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private getRandomLevel;
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private insertNode;
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private searchLayer;
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/**
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* OPTIMIZED searchLayer using heap-based priority queues
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* Performance: O(log n) per operation vs O(n log n) for Array.sort()
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* Expected speedup: 3-5x for large result sets
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*/
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private searchLayerOptimized;
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private selectNeighbors;
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private pruneConnections;
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private distance;
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private cosineDistance;
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/**
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* OPTIMIZED: Cosine distance using pre-normalized vectors
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* Only requires dot product (no sqrt operations)
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* Performance: O(n) with ~2x speedup over standard cosine
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*/
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private cosineDistanceNormalized;
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/**
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* Normalize a vector to unit length for O(1) cosine similarity
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*/
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private normalizeVector;
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/**
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* OPTIMIZED distance calculation that uses pre-normalized vectors when available
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*/
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private distanceOptimized;
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private euclideanDistance;
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private dotProductDistance;
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private manhattanDistance;
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}
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export default HNSWIndex;
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//# sourceMappingURL=hnsw-index.d.ts.map
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