781 lines
27 KiB
JavaScript
781 lines
27 KiB
JavaScript
/**
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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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/**
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* Binary Min Heap for O(log n) priority queue operations
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* Used for candidate selection in HNSW search
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*/
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class BinaryMinHeap {
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heap = [];
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get size() {
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return this.heap.length;
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}
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insert(item, priority) {
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this.heap.push({ item, priority });
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this.bubbleUp(this.heap.length - 1);
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}
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extractMin() {
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if (this.heap.length === 0)
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return undefined;
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const min = this.heap[0].item;
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const last = this.heap.pop();
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if (this.heap.length > 0) {
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this.heap[0] = last;
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this.bubbleDown(0);
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}
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return min;
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}
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peek() {
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return this.heap[0]?.item;
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}
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peekPriority() {
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return this.heap[0]?.priority;
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}
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isEmpty() {
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return this.heap.length === 0;
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}
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toArray() {
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return this.heap
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.slice()
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.sort((a, b) => a.priority - b.priority)
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.map((entry) => entry.item);
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}
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bubbleUp(index) {
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while (index > 0) {
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const parent = Math.floor((index - 1) / 2);
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if (this.heap[parent].priority <= this.heap[index].priority)
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break;
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[this.heap[parent], this.heap[index]] = [this.heap[index], this.heap[parent]];
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index = parent;
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}
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}
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bubbleDown(index) {
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const length = this.heap.length;
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while (true) {
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let smallest = index;
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const left = 2 * index + 1;
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const right = 2 * index + 2;
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if (left < length && this.heap[left].priority < this.heap[smallest].priority) {
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smallest = left;
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}
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if (right < length && this.heap[right].priority < this.heap[smallest].priority) {
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smallest = right;
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}
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if (smallest === index)
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break;
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[this.heap[smallest], this.heap[index]] = [this.heap[index], this.heap[smallest]];
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index = smallest;
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}
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}
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}
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/**
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* Binary Max Heap for bounded top-k tracking
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* Keeps track of k smallest elements by evicting largest when full
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*/
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class BinaryMaxHeap {
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heap = [];
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maxSize;
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constructor(maxSize = Infinity) {
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this.maxSize = maxSize;
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}
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get size() {
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return this.heap.length;
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}
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insert(item, priority) {
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// If at capacity and new item is worse than worst, reject
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if (this.heap.length >= this.maxSize && priority >= this.heap[0]?.priority) {
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return false;
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}
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if (this.heap.length >= this.maxSize) {
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// Replace max element
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this.heap[0] = { item, priority };
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this.bubbleDown(0);
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}
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else {
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this.heap.push({ item, priority });
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this.bubbleUp(this.heap.length - 1);
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}
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return true;
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}
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peekMax() {
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return this.heap[0]?.item;
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}
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peekMaxPriority() {
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return this.heap[0]?.priority ?? Infinity;
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}
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extractMax() {
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if (this.heap.length === 0)
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return undefined;
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const max = this.heap[0].item;
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const last = this.heap.pop();
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if (this.heap.length > 0) {
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this.heap[0] = last;
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this.bubbleDown(0);
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}
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return max;
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}
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isEmpty() {
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return this.heap.length === 0;
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}
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toSortedArray() {
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return this.heap.slice().sort((a, b) => a.priority - b.priority);
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}
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bubbleUp(index) {
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while (index > 0) {
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const parent = Math.floor((index - 1) / 2);
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if (this.heap[parent].priority >= this.heap[index].priority)
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break;
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[this.heap[parent], this.heap[index]] = [this.heap[index], this.heap[parent]];
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index = parent;
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}
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}
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bubbleDown(index) {
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const length = this.heap.length;
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while (true) {
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let largest = index;
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const left = 2 * index + 1;
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const right = 2 * index + 2;
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if (left < length && this.heap[left].priority > this.heap[largest].priority) {
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largest = left;
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}
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if (right < length && this.heap[right].priority > this.heap[largest].priority) {
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largest = right;
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}
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if (largest === index)
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break;
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[this.heap[largest], this.heap[index]] = [this.heap[index], this.heap[largest]];
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index = largest;
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}
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}
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}
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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 class HNSWIndex extends EventEmitter {
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config;
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nodes = new Map();
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entryPoint = null;
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maxLevel = 0;
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levelMult;
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// Performance tracking
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stats = {
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searchCount: 0,
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totalSearchTime: 0,
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insertCount: 0,
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totalInsertTime: 0,
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buildStartTime: 0,
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};
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// Quantization support
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quantizer = null;
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constructor(config = {}) {
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super();
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this.config = this.mergeConfig(config);
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this.levelMult = 1 / Math.log(this.config.M);
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if (this.config.quantization) {
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this.quantizer = new Quantizer(this.config.quantization, this.config.dimensions);
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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 addPoint(id, vector) {
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const startTime = performance.now();
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if (vector.length !== this.config.dimensions) {
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throw new Error(`Vector dimension mismatch: expected ${this.config.dimensions}, got ${vector.length}`);
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}
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if (this.nodes.size >= this.config.maxElements) {
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throw new Error('Index is full, cannot add more elements');
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}
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// Quantize if enabled
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const storedVector = this.quantizer
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? this.quantizer.encode(vector)
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: vector;
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// Pre-normalize vector for O(1) cosine similarity
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const normalizedVector = this.config.metric === 'cosine'
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? this.normalizeVector(storedVector)
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: null;
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// Generate random level for new node
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const level = this.getRandomLevel();
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const node = {
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id,
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vector: storedVector,
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normalizedVector,
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connections: new Map(),
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level,
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};
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// Initialize connection sets for each layer
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for (let l = 0; l <= level; l++) {
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node.connections.set(l, new Set());
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}
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if (this.entryPoint === null) {
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// First node
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this.entryPoint = id;
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this.maxLevel = level;
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this.nodes.set(id, node);
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}
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else {
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// Insert new node into the graph
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await this.insertNode(node);
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}
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const duration = performance.now() - startTime;
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this.stats.insertCount++;
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this.stats.totalInsertTime += duration;
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this.emit('point:added', { id, level, duration });
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}
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/**
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* Search for k nearest neighbors
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*/
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async search(query, k, ef) {
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const startTime = performance.now();
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if (query.length !== this.config.dimensions) {
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throw new Error(`Query dimension mismatch: expected ${this.config.dimensions}, got ${query.length}`);
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}
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if (this.entryPoint === null) {
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return [];
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}
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const searchEf = ef || Math.max(k, this.config.efConstruction);
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// Quantize query if needed
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const queryVector = this.quantizer
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? this.quantizer.encode(query)
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: query;
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// Pre-normalize query for O(1) cosine similarity
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const normalizedQuery = this.config.metric === 'cosine'
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? this.normalizeVector(queryVector)
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: null;
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// Start from entry point and search down the layers
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let currentNode = this.entryPoint;
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let currentDist = this.distanceOptimized(queryVector, normalizedQuery, this.nodes.get(currentNode));
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// Search through layers from top to 1
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for (let level = this.maxLevel; level > 0; level--) {
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const layerResult = this.searchLayerOptimized(queryVector, normalizedQuery, currentNode, 1, level);
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currentNode = layerResult[0]?.id || currentNode;
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currentDist = this.distanceOptimized(queryVector, normalizedQuery, this.nodes.get(currentNode));
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}
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// Search layer 0 with ef candidates using heap-based search
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const candidates = this.searchLayerOptimized(queryVector, normalizedQuery, currentNode, searchEf, 0);
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// Return top k results (already sorted by heap)
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const results = candidates.slice(0, k);
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const duration = performance.now() - startTime;
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this.stats.searchCount++;
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this.stats.totalSearchTime += duration;
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return results;
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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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async searchWithFilters(query, k, filter, ef) {
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// Over-fetch to account for filtered results
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const overFetchFactor = 3;
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const candidates = await this.search(query, k * overFetchFactor, ef);
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return candidates
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.filter((c) => filter(c.id))
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.slice(0, k);
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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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async removePoint(id) {
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const node = this.nodes.get(id);
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if (!node) {
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return false;
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}
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// Remove all connections to this node
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for (let level = 0; level <= node.level; level++) {
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const connections = node.connections.get(level);
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if (connections) {
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for (const connectedId of connections) {
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const connectedNode = this.nodes.get(connectedId);
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if (connectedNode) {
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connectedNode.connections.get(level)?.delete(id);
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}
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}
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}
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}
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this.nodes.delete(id);
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// Update entry point if needed
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if (this.entryPoint === id) {
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if (this.nodes.size === 0) {
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this.entryPoint = null;
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this.maxLevel = 0;
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}
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else {
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// Find new entry point with highest level
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let newEntry = null;
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let newMaxLevel = 0;
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for (const [nodeId, n] of this.nodes) {
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if (newEntry === null || n.level > newMaxLevel) {
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newMaxLevel = n.level;
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newEntry = nodeId;
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}
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}
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this.entryPoint = newEntry;
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this.maxLevel = newMaxLevel;
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}
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}
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this.emit('point:removed', { id });
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return true;
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}
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/**
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* Rebuild the index from scratch
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*/
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async rebuild(entries) {
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this.stats.buildStartTime = performance.now();
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this.nodes.clear();
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this.entryPoint = null;
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this.maxLevel = 0;
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for (const entry of entries) {
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await this.addPoint(entry.id, entry.vector);
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}
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const buildTime = performance.now() - this.stats.buildStartTime;
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this.emit('index:rebuilt', {
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vectorCount: this.nodes.size,
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buildTime,
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});
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}
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/**
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* Get index statistics
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*/
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getStats() {
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const vectorCount = this.nodes.size;
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const avgSearchTime = this.stats.searchCount > 0
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? this.stats.totalSearchTime / this.stats.searchCount
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: 0;
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// Estimate memory usage
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const bytesPerVector = this.config.dimensions * 4; // Float32 = 4 bytes
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const connectionOverhead = this.config.M * 8 * (this.maxLevel + 1); // Approximate
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const memoryUsage = vectorCount * (bytesPerVector + connectionOverhead);
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return {
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vectorCount,
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memoryUsage,
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avgSearchTime,
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buildTime: performance.now() - this.stats.buildStartTime,
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compressionRatio: this.quantizer?.getCompressionRatio() || 1.0,
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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.nodes.clear();
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this.entryPoint = null;
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this.maxLevel = 0;
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this.stats = {
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searchCount: 0,
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totalSearchTime: 0,
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insertCount: 0,
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totalInsertTime: 0,
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buildStartTime: 0,
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};
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}
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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) {
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return this.nodes.has(id);
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}
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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() {
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return this.nodes.size;
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}
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// ===== Private Methods =====
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mergeConfig(config) {
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return {
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dimensions: config.dimensions || 1536, // OpenAI embedding size
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M: config.M || 16,
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efConstruction: config.efConstruction || 200,
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maxElements: config.maxElements || 1000000,
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metric: config.metric || 'cosine',
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quantization: config.quantization,
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};
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}
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getRandomLevel() {
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let level = 0;
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while (Math.random() < 0.5 && level < 16) {
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level++;
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}
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return level;
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}
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async insertNode(node) {
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const query = node.vector;
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const normalizedQuery = node.normalizedVector;
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let currentNode = this.entryPoint;
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let currentDist = this.distanceOptimized(query, normalizedQuery, this.nodes.get(currentNode));
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// Find entry point for the node's level
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for (let level = this.maxLevel; level > node.level; level--) {
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const result = this.searchLayerOptimized(query, normalizedQuery, currentNode, 1, level);
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if (result.length > 0 && result[0].distance < currentDist) {
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currentNode = result[0].id;
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currentDist = result[0].distance;
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}
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}
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// Insert at each level from node.level down to 0
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for (let level = Math.min(node.level, this.maxLevel); level >= 0; level--) {
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const neighbors = this.searchLayerOptimized(query, normalizedQuery, currentNode, this.config.efConstruction, level);
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// Select M best neighbors
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const selectedNeighbors = this.selectNeighbors(node.id, query, neighbors, this.config.M);
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// Add connections
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for (const neighbor of selectedNeighbors) {
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node.connections.get(level).add(neighbor.id);
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this.nodes.get(neighbor.id)?.connections.get(level)?.add(node.id);
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// Prune connections if over limit
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const neighborNode = this.nodes.get(neighbor.id);
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if (neighborNode) {
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const neighborConns = neighborNode.connections.get(level);
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if (neighborConns.size > this.config.M * 2) {
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this.pruneConnections(neighborNode, level, this.config.M);
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}
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}
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}
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if (neighbors.length > 0) {
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currentNode = neighbors[0].id;
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}
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}
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this.nodes.set(node.id, node);
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// Update max level if needed
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if (node.level > this.maxLevel) {
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this.maxLevel = node.level;
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this.entryPoint = node.id;
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}
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}
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async searchLayer(query, entryPoint, ef, level) {
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const visited = new Set([entryPoint]);
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const candidates = [];
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const results = [];
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const entryDist = this.distance(query, this.nodes.get(entryPoint).vector);
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candidates.push({ id: entryPoint, distance: entryDist });
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results.push({ id: entryPoint, distance: entryDist });
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while (candidates.length > 0) {
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// Get closest candidate
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candidates.sort((a, b) => a.distance - b.distance);
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const current = candidates.shift();
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// Check termination condition
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const worstResult = results.length > 0
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? Math.max(...results.map((r) => r.distance))
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: Infinity;
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if (current.distance > worstResult && results.length >= ef) {
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break;
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}
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// Explore neighbors
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const node = this.nodes.get(current.id);
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if (!node)
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continue;
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const connections = node.connections.get(level);
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if (!connections)
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continue;
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for (const neighborId of connections) {
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if (visited.has(neighborId))
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continue;
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visited.add(neighborId);
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const neighborNode = this.nodes.get(neighborId);
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if (!neighborNode)
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continue;
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const distance = this.distance(query, neighborNode.vector);
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if (results.length < ef || distance < worstResult) {
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candidates.push({ id: neighborId, distance });
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results.push({ id: neighborId, distance });
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// Keep results bounded
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if (results.length > ef) {
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results.sort((a, b) => a.distance - b.distance);
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results.pop();
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}
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}
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}
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}
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return results.sort((a, b) => a.distance - b.distance);
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}
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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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searchLayerOptimized(query, normalizedQuery, entryPoint, ef, level) {
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const visited = new Set([entryPoint]);
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// Min-heap for candidates (closest first for expansion)
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const candidates = new BinaryMinHeap();
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// Max-heap for results (bounded size, tracks worst distance efficiently)
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const results = new BinaryMaxHeap(ef);
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const entryNode = this.nodes.get(entryPoint);
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const entryDist = this.distanceOptimized(query, normalizedQuery, entryNode);
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candidates.insert(entryPoint, entryDist);
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results.insert(entryPoint, entryDist);
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while (!candidates.isEmpty()) {
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// Get closest candidate - O(log n)
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const currentDist = candidates.peekPriority();
|
|
const currentId = candidates.extractMin();
|
|
// Check termination: if closest candidate is worse than worst result, stop
|
|
const worstResultDist = results.peekMaxPriority();
|
|
if (currentDist > worstResultDist && results.size >= ef) {
|
|
break;
|
|
}
|
|
// Explore neighbors
|
|
const node = this.nodes.get(currentId);
|
|
if (!node)
|
|
continue;
|
|
const connections = node.connections.get(level);
|
|
if (!connections)
|
|
continue;
|
|
for (const neighborId of connections) {
|
|
if (visited.has(neighborId))
|
|
continue;
|
|
visited.add(neighborId);
|
|
const neighborNode = this.nodes.get(neighborId);
|
|
if (!neighborNode)
|
|
continue;
|
|
const distance = this.distanceOptimized(query, normalizedQuery, neighborNode);
|
|
// Only add if within threshold or results not full
|
|
if (results.size < ef || distance < worstResultDist) {
|
|
candidates.insert(neighborId, distance);
|
|
// Max-heap handles size bounding automatically - O(log n)
|
|
results.insert(neighborId, distance);
|
|
}
|
|
}
|
|
}
|
|
// Return sorted results
|
|
return results.toSortedArray().map(({ item, priority }) => ({
|
|
id: item,
|
|
distance: priority,
|
|
}));
|
|
}
|
|
selectNeighbors(nodeId, query, candidates, M) {
|
|
// Simple selection: take M closest
|
|
return candidates
|
|
.filter((c) => c.id !== nodeId)
|
|
.sort((a, b) => a.distance - b.distance)
|
|
.slice(0, M);
|
|
}
|
|
pruneConnections(node, level, maxConnections) {
|
|
const connections = node.connections.get(level);
|
|
if (!connections || connections.size <= maxConnections)
|
|
return;
|
|
// Calculate distances to all connections
|
|
const distances = [];
|
|
for (const connId of connections) {
|
|
const connNode = this.nodes.get(connId);
|
|
if (connNode) {
|
|
distances.push({
|
|
id: connId,
|
|
distance: this.distance(node.vector, connNode.vector),
|
|
});
|
|
}
|
|
}
|
|
// Keep only the closest ones
|
|
distances.sort((a, b) => a.distance - b.distance);
|
|
const toKeep = new Set(distances.slice(0, maxConnections).map((d) => d.id));
|
|
// Remove excess connections
|
|
for (const connId of connections) {
|
|
if (!toKeep.has(connId)) {
|
|
connections.delete(connId);
|
|
this.nodes.get(connId)?.connections.get(level)?.delete(node.id);
|
|
}
|
|
}
|
|
}
|
|
distance(a, b) {
|
|
switch (this.config.metric) {
|
|
case 'cosine':
|
|
return this.cosineDistance(a, b);
|
|
case 'euclidean':
|
|
return this.euclideanDistance(a, b);
|
|
case 'dot':
|
|
return this.dotProductDistance(a, b);
|
|
case 'manhattan':
|
|
return this.manhattanDistance(a, b);
|
|
default:
|
|
return this.cosineDistance(a, b);
|
|
}
|
|
}
|
|
cosineDistance(a, b) {
|
|
let dotProduct = 0;
|
|
let normA = 0;
|
|
let normB = 0;
|
|
for (let i = 0; i < a.length; i++) {
|
|
dotProduct += a[i] * b[i];
|
|
normA += a[i] * a[i];
|
|
normB += b[i] * b[i];
|
|
}
|
|
const similarity = dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
|
|
return 1 - similarity; // Convert to distance
|
|
}
|
|
/**
|
|
* OPTIMIZED: Cosine distance using pre-normalized vectors
|
|
* Only requires dot product (no sqrt operations)
|
|
* Performance: O(n) with ~2x speedup over standard cosine
|
|
*/
|
|
cosineDistanceNormalized(a, b) {
|
|
let dotProduct = 0;
|
|
for (let i = 0; i < a.length; i++) {
|
|
dotProduct += a[i] * b[i];
|
|
}
|
|
// For normalized vectors: cosine_similarity = dot_product
|
|
// Return distance (1 - similarity)
|
|
return 1 - dotProduct;
|
|
}
|
|
/**
|
|
* Normalize a vector to unit length for O(1) cosine similarity
|
|
*/
|
|
normalizeVector(vector) {
|
|
let norm = 0;
|
|
for (let i = 0; i < vector.length; i++) {
|
|
norm += vector[i] * vector[i];
|
|
}
|
|
norm = Math.sqrt(norm);
|
|
if (norm === 0) {
|
|
return vector; // Return as-is if zero vector
|
|
}
|
|
const normalized = new Float32Array(vector.length);
|
|
for (let i = 0; i < vector.length; i++) {
|
|
normalized[i] = vector[i] / norm;
|
|
}
|
|
return normalized;
|
|
}
|
|
/**
|
|
* OPTIMIZED distance calculation that uses pre-normalized vectors when available
|
|
*/
|
|
distanceOptimized(query, normalizedQuery, node) {
|
|
// Use optimized path for cosine with pre-normalized vectors
|
|
if (this.config.metric === 'cosine' &&
|
|
normalizedQuery !== null &&
|
|
node.normalizedVector !== null) {
|
|
return this.cosineDistanceNormalized(normalizedQuery, node.normalizedVector);
|
|
}
|
|
// Fall back to standard distance calculation
|
|
return this.distance(query, node.vector);
|
|
}
|
|
euclideanDistance(a, b) {
|
|
let sum = 0;
|
|
for (let i = 0; i < a.length; i++) {
|
|
const diff = a[i] - b[i];
|
|
sum += diff * diff;
|
|
}
|
|
return Math.sqrt(sum);
|
|
}
|
|
dotProductDistance(a, b) {
|
|
let dotProduct = 0;
|
|
for (let i = 0; i < a.length; i++) {
|
|
dotProduct += a[i] * b[i];
|
|
}
|
|
// Negative because higher dot product = more similar
|
|
return -dotProduct;
|
|
}
|
|
manhattanDistance(a, b) {
|
|
let sum = 0;
|
|
for (let i = 0; i < a.length; i++) {
|
|
sum += Math.abs(a[i] - b[i]);
|
|
}
|
|
return sum;
|
|
}
|
|
}
|
|
/**
|
|
* Quantizer for vector compression
|
|
*/
|
|
class Quantizer {
|
|
config;
|
|
dimensions;
|
|
constructor(config, dimensions) {
|
|
this.config = config;
|
|
this.dimensions = dimensions;
|
|
}
|
|
/**
|
|
* Encode a vector using quantization
|
|
*/
|
|
encode(vector) {
|
|
switch (this.config.type) {
|
|
case 'binary':
|
|
return this.binaryQuantize(vector);
|
|
case 'scalar':
|
|
return this.scalarQuantize(vector);
|
|
case 'product':
|
|
return this.productQuantize(vector);
|
|
default:
|
|
return vector;
|
|
}
|
|
}
|
|
/**
|
|
* Get compression ratio
|
|
*/
|
|
getCompressionRatio() {
|
|
switch (this.config.type) {
|
|
case 'binary':
|
|
return 32; // 32x compression (32 bits -> 1 bit per dimension)
|
|
case 'scalar':
|
|
return 32 / (this.config.bits || 8);
|
|
case 'product':
|
|
return this.config.subquantizers || 8;
|
|
default:
|
|
return 1;
|
|
}
|
|
}
|
|
binaryQuantize(vector) {
|
|
// Simple binary quantization: > 0 becomes 1, <= 0 becomes 0
|
|
// Stored in packed format in a smaller Float32Array
|
|
const packedLength = Math.ceil(vector.length / 32);
|
|
const packed = new Float32Array(packedLength);
|
|
for (let i = 0; i < vector.length; i++) {
|
|
const packedIndex = Math.floor(i / 32);
|
|
const bitPosition = i % 32;
|
|
if (vector[i] > 0) {
|
|
packed[packedIndex] = (packed[packedIndex] || 0) | (1 << bitPosition);
|
|
}
|
|
}
|
|
return packed;
|
|
}
|
|
scalarQuantize(vector) {
|
|
// Find min/max for normalization
|
|
let min = Infinity;
|
|
let max = -Infinity;
|
|
for (let i = 0; i < vector.length; i++) {
|
|
if (vector[i] < min)
|
|
min = vector[i];
|
|
if (vector[i] > max)
|
|
max = vector[i];
|
|
}
|
|
const range = max - min || 1;
|
|
const bits = this.config.bits || 8;
|
|
const levels = Math.pow(2, bits);
|
|
// Quantize each value
|
|
const quantized = new Float32Array(vector.length + 2); // +2 for min/range
|
|
quantized[0] = min;
|
|
quantized[1] = range;
|
|
for (let i = 0; i < vector.length; i++) {
|
|
const normalized = (vector[i] - min) / range;
|
|
quantized[i + 2] = Math.round(normalized * (levels - 1));
|
|
}
|
|
return quantized;
|
|
}
|
|
productQuantize(vector) {
|
|
// Simplified product quantization
|
|
// In production, would use trained codebooks
|
|
const subquantizers = this.config.subquantizers || 8;
|
|
const subvectorSize = Math.ceil(vector.length / subquantizers);
|
|
const quantized = new Float32Array(subquantizers);
|
|
for (let i = 0; i < subquantizers; i++) {
|
|
let sum = 0;
|
|
const start = i * subvectorSize;
|
|
const end = Math.min(start + subvectorSize, vector.length);
|
|
for (let j = start; j < end; j++) {
|
|
sum += vector[j];
|
|
}
|
|
quantized[i] = sum / (end - start);
|
|
}
|
|
return quantized;
|
|
}
|
|
}
|
|
export default HNSWIndex;
|
|
//# sourceMappingURL=hnsw-index.js.map
|