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class BinaryHeap {\n private data: number[];\n private size: number;\n\n constructor(data: number[] = []) {\n this.data = data.slice(); // 创建副本避免外部修改\n this.size = this.data.length;\n\n if (this.size > 0) {\n this.heapify();\n }\n }\n\n get length(): number {\n return this.size;\n }\n\n /**\n * 插入元素 - 优化版本\n */\n push(item: number): void {\n const data = this.data;\n const pos = this.size++;\n \n data[pos] = item;\n this.upHeap(pos, data);\n }\n\n /**\n * 批量插入 - 保持V1简洁性的高效实现\n */\n pushBulk(items: number[]): void {\n if (items.length === 0) return;\n\n // 预分配空间避免多次扩容\n const currentSize = this.size;\n const newSize = currentSize + items.length;\n \n // 确保数组有足够容量\n if (newSize > this.data.length) {\n const newCapacity = Math.max(newSize, this.data.length * 2);\n const newData = new Array(newCapacity);\n for (let i = 0; i < currentSize; i++) {\n newData[i] = this.data[i]!;\n }\n this.data = newData;\n }\n\n // 批量策略：根据数据量选择最优方法\n if (items.length < currentSize / 8) {\n // 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item = data[pos]!;\n \n // 展开循环的前几次迭代，减少分支开销\n if (pos > 0) {\n const parent = (pos - 1) >>> 1;\n const parentValue = data[parent]!;\n if (item < parentValue) {\n data[pos] = parentValue;\n pos = parent;\n \n // 继续向上\n while (pos > 0) {\n const nextParent = (pos - 1) >>> 1;\n const nextParentValue = data[nextParent]!;\n if (item >= nextParentValue) break;\n \n data[pos] = nextParentValue;\n pos = nextParent;\n }\n }\n }\n \n data[pos] = item;\n }\n\n /**\n * 下沉操作 - 超级优化版本\n */\n private downHeap(pos: number, data: number[], size: number): void {\n const halfLength = size >>> 1;\n const item = data[pos]!;\n \n while (pos < halfLength) {\n let bestChild = (pos << 1) + 1; // 左子节点\n let bestValue = data[bestChild]!;\n const rightChild = bestChild + 1;\n \n // 优化：先检查右子节点是否存在，再比较值\n if (rightChild < size) {\n const rightValue = data[rightChild]!;\n if (rightValue < bestValue) {\n bestChild = rightChild;\n bestValue = rightValue;\n }\n }\n \n // 如果当前项已经小于等于最佳子节点，结束\n if (item <= bestValue) break;\n \n data[pos] = bestValue;\n pos = bestChild;\n }\n \n data[pos] = item;\n }\n\n /**\n * 检查堆的有效性（调试用）\n */\n isValid(): boolean {\n const data = this.data;\n const size = this.size;\n \n for (let i = 0; i < (size >>> 1); i++) {\n const left = (i << 1) + 1;\n const right = left + 1;\n \n if (left < size && data[i]! > data[left]!) return false;\n if (right < size && data[i]! > data[right]!) return false;\n }\n \n return true;\n }\n\n /**\n * 转换为数组（调试用）\n */\n toArray(): number[] {\n const result: number[] = [];\n const tempHeap = new BinaryHeap(this.data.slice(0, this.size));\n \n while (tempHeap.length > 0) {\n result.push(tempHeap.pop()!);\n }\n \n return result;\n }\n\n /**\n * 清空堆\n */\n clear(): void {\n this.size = 0;\n }\n}\n\n/**\n * 泛型版本 - 支持自定义比较函数，但性能稍低\n */\nexport class BinaryHeapGeneric {\n private data: T[];\n private size: number;\n private compare: (a: T, b: T) => number;\n\n constructor(data: T[] = [], compare: (a: T, b: T) => number = defaultCompare) {\n this.data = data.slice();\n this.size = this.data.length;\n this.compare = compare;\n\n if (this.size > 0) {\n this.heapify();\n }\n }\n\n get length(): number {\n return this.size;\n }\n\n push(item: T): void {\n const data = this.data;\n const pos = this.size++;\n \n data[pos] = item;\n this.upHeap(pos, data);\n }\n\n /**\n * 批量插入 - 泛型版本的高效实现\n */\n pushBulk(items: T[]): void {\n if (items.length === 0) return;\n\n // 预分配空间避免多次扩容\n const currentSize = this.size;\n const newSize = currentSize + items.length;\n \n // 确保数组有足够容量\n if (newSize > this.data.length) {\n const newCapacity = Math.max(newSize, this.data.length * 2);\n const newData = new Array(newCapacity);\n for (let i = 0; i < currentSize; i++) {\n newData[i] = this.data[i]!;\n }\n this.data = newData;\n }\n\n // 批量策略：根据数据量选择最优方法\n if (items.length < currentSize / 8) {\n // 小批量：逐个插入（利用单操作优势）\n for (const item of items) {\n this.push(item);\n }\n } else {\n // 大批量：直接追加 + heapify\n for (let i = 0; i < items.length; i++) {\n this.data[currentSize + i] = items[i]!;\n }\n this.size = newSize;\n this.heapify();\n }\n }\n\n pop(): T | undefined {\n const size = this.size;\n if (size === 0) return undefined;\n\n const data = this.data;\n const top = data[0]!;\n \n const newSize = size - 1;\n this.size = newSize;\n \n if (newSize > 0) {\n data[0] = data[newSize]!;\n this.downHeap(0, data, newSize);\n }\n\n return top;\n }\n\n peek(): T | undefined {\n return this.data[0];\n }\n\n private heapify(): void {\n const data = this.data;\n const size = this.size;\n \n for (let i = (size >>> 1) - 1; i >= 0; i--) {\n this.downHeap(i, data, size);\n }\n }\n\n private upHeap(pos: number, data: T[]): void {\n const item = data[pos]!;\n const compare = this.compare;\n \n while (pos > 0) {\n const parent = (pos - 1) >>> 1;\n const parentValue = data[parent]!;\n if (compare(item, parentValue) >= 0) break;\n \n data[pos] = parentValue;\n pos = parent;\n }\n \n data[pos] = item;\n }\n\n private downHeap(pos: number, data: T[], size: number): void {\n const halfLength = size >>> 1;\n const item = data[pos]!;\n const compare = this.compare;\n \n while (pos < halfLength) {\n let bestChild = (pos << 1) + 1;\n let bestValue = data[bestChild]!;\n const rightChild = bestChild + 1;\n \n if (rightChild < size && compare(data[rightChild]!, bestValue) < 0) {\n bestChild = rightChild;\n bestValue = data[rightChild]!;\n }\n \n if (compare(item, bestValue) <= 0) break;\n \n data[pos] = bestValue;\n pos = bestChild;\n }\n \n data[pos] = item;\n }\n}\n\nfunction defaultCompare(a: T, b: T): number {\n return a < b ? -1 : a > b ? 1 : 0;\n} ","/**\n * MidiHeap - 中等容量优化的固定大小二叉堆\n * \n * 基于MicroHeap的设计，但容量可配置。\n * 专为HNSW算法中的中等大小集合（例如64-1024个元素）优化，\n * 结合了数组的缓存友好性与预分配带来的高性能。\n */\nexport class MidiHeapGeneric {\n private data: T[];\n private capacity: number;\n private size = 0;\n private compare: (a: T, b: T) => number;\n\n constructor(capacity: number, compare: (a: T, b: T) => number, initialData?: T[]) {\n if (capacity <= 0) {\n throw new Error('Heap capacity must be greater than 0');\n }\n this.capacity = capacity;\n this.data = new Array(capacity);\n this.compare = compare;\n\n if (initialData && initialData.length > 0) {\n const initialSize = Math.min(initialData.length, this.capacity);\n this.size = initialSize;\n for (let i = 0; i < initialSize; i++) {\n this.data[i] = initialData[i]!;\n }\n this.heapify();\n }\n }\n \n get length(): number {\n return this.size;\n }\n\n isFull(): boolean {\n return this.size >= this.capacity;\n }\n\n /**\n * 向堆中添加一个元素。\n * @注意 如果堆已满，此操作会静默失败。请使用 isFull() 检查。\n */\n push(item: T): void {\n if (this.isFull()) {\n return;\n }\n\n const data = this.data;\n const compare = this.compare;\n let pos = this.size++;\n \n while (pos > 0) {\n const parent = (pos - 1) >>> 1;\n if (compare(item, data[parent]!) >= 0) break;\n \n data[pos] = data[parent]!;\n pos = parent;\n }\n \n data[pos] = item;\n }\n\n pop(): T | undefined {\n if (this.size === 0) return undefined;\n\n const data = this.data;\n const top = data[0]!;\n const newSize = --this.size;\n \n if (newSize > 0) {\n data[0] = data[newSize]!;\n this.downHeap(0, newSize);\n }\n\n return top;\n }\n\n /**\n * 替换堆顶元素，比 pop() + push() 更高效。\n * @returns 返回被替换掉的原堆顶元素。\n */\n replace(item: T): T {\n const top = this.data[0]!;\n this.data[0] = item;\n this.downHeap(0, this.size);\n return top;\n }\n\n private downHeap(pos: number, size: number): void {\n const data = this.data;\n const compare = this.compare;\n const item = data[pos]!;\n \n while (true) {\n const leftChild = (pos << 1) + 1;\n if (leftChild >= size) break;\n \n let bestChild = leftChild;\n \n const rightChild = leftChild + 1;\n if (rightChild < size && compare(data[rightChild]!, data[leftChild]!) < 0) {\n bestChild = rightChild;\n }\n \n if (compare(item, data[bestChild]!) <= 0) break;\n \n data[pos] = data[bestChild]!;\n pos = bestChild;\n }\n \n data[pos] = item;\n }\n\n peek(): T | undefined {\n return this.size > 0 ? this.data[0] : undefined;\n }\n\n private heapify(): void {\n const size = this.size;\n for (let i = (size >>> 1) - 1; i >= 0; i--) {\n this.downHeap(i, size);\n }\n }\n\n clear(): void {\n this.size = 0;\n }\n \n toArray(): T[] {\n return this.data.slice(0, this.size);\n }\n\n toSortedArray(): T[] {\n // 哥哥说得对，这里直接复制数组然后排序，比新建一个堆实例效率高得多！\n // 1. 复制数组的有效部分\n const sorted = this.data.slice(0, this.size);\n \n // 2. 使用与堆相同的比较逻辑进行排序。\n sorted.sort(this.compare);\n\n // 3. 不再画蛇添足地反转，将排序结果直接返回\n return sorted;\n }\n} ","import { BinaryHeapGeneric } from './binary-heap';\nimport { MidiHeapGeneric } from './midi-heap';\n\nexport interface HNSWConfig {\n M: number;\n efConstruction: number;\n distanceFunction: (a: T, b: T) => number;\n distanceToQuery?: (query: T, target: T) => number; // 可选，如果未提供则使用distanceFunction\n}\n\ninterface Neighbor {\n idx: number;\n distance: number;\n}\n\nexport interface HNSWIndex {\n insertNode: (vector: T) => void;\n search: (queryVector: T, k: number, efSearch?: number) => Neighbor[];\n deleteNode: (nodeIdx: number) => boolean;\n getStats: () => { \n nodeCount: number; \n activeNodeCount: number;\n deletedNodeCount: number;\n entryPoint: { idx: number; level: number } \n };\n}\n\nexport function createHNSWIndex(config: HNSWConfig): HNSWIndex {\n const { M, efConstruction, distanceFunction, distanceToQuery } = config;\n\n // --- 数据存储 ---\n const vectors: T[] = [];\n const neighbors: number[][][] = []; // [nodeId][level] -> [neighborId, ...]\n const entryPoint = { idx: -1, level: -1 };\n\n // --- 删除功能相关数据结构 ---\n const deletedNodes: Set = new Set();\n const nodeToLevels: Map = new Map(); // 记录每个节点的最高层级\n\n // --- 性能优化组件 ---\n // 在构造时分配，避免重复创建\n let visited: Uint8Array | null = null;\n const maxNodes = 10000; // 预设最大容量\n visited = new Uint8Array(maxNodes);\n\n // --- 核心算法 ---\n\n /**\n * 检查节点是否有效（未被删除）\n */\n function isValidNode(idx: number): boolean {\n return idx >= 0 && idx < vectors.length && !deletedNodes.has(idx);\n }\n\n /**\n * 计算两个已存在节点之间的距离\n */\n function distance(idxA: number, idxB: number): number {\n // 检查节点有效性\n if (!isValidNode(idxA) || !isValidNode(idxB)) {\n return Infinity; // 无效节点返回无穷大距离\n }\n\n return distanceFunction(vectors[idxA]!, vectors[idxB]!);\n }\n\n /**\n * 计算查询向量到目标节点的距离\n */\n function distanceToTarget(queryVector: T, targetIdx: number): number {\n // 检查目标节点有效性\n if (!isValidNode(targetIdx)) {\n return Infinity; // 无效节点返回无穷大距离\n }\n\n const targetVector = vectors[targetIdx]!;\n \n // 如果提供了专门的distanceToQuery函数，使用它\n if (distanceToQuery) {\n return distanceToQuery(queryVector, targetVector);\n }\n \n // 否则使用通用的distanceFunction\n return distanceFunction(queryVector, targetVector);\n }\n\n /**\n * 在指定层级上搜索最近的 ef 个邻居 - 使用优化的堆算法\n */\n function searchLayerWithQuery(\n queryVector: T,\n startNodeIdx: number,\n level: number,\n ef: number\n ): Neighbor[] {\n if (!visited) throw new Error(\"Visited set not initialized\");\n visited.fill(0);\n\n // 检查起始节点有效性\n if (!isValidNode(startNodeIdx)) {\n return []; // 如果起始节点无效，返回空结果\n }\n\n // 使用动态二叉堆处理大小不定的候选集\n const candidates = new BinaryHeapGeneric([], (a, b) => a.distance - b.distance);\n // 使用固定容量的MidiHeap处理Top-K结果集，性能更高\n const results = new MidiHeapGeneric(ef, (a, b) => b.distance - a.distance);\n\n const startNodeDist = distanceToTarget(queryVector, startNodeIdx);\n visited[startNodeIdx] = 1;\n candidates.push({ idx: startNodeIdx, distance: startNodeDist });\n results.push({ idx: startNodeIdx, distance: startNodeDist });\n\n while (candidates.length > 0) {\n const bestCandidate = candidates.peek()!;\n const farthestResult = results.peek();\n\n // MidiHeap优化 - 剪枝逻辑更清晰\n if (farthestResult && bestCandidate.distance > farthestResult.distance && results.isFull()) {\n break; \n }\n const cand = candidates.pop()!;\n\n const nodeNeighbors = neighbors[cand.idx]?.[level] || [];\n for (const neighborIdx of nodeNeighbors) {\n // 只处理有效的邻居节点\n if (visited[neighborIdx] === 0 && isValidNode(neighborIdx)) {\n visited[neighborIdx] = 1;\n const dist = distanceToTarget(queryVector, neighborIdx);\n const currentFarthest = results.peek();\n \n // MidiHeap优化 - 使用isFull和replace API，效率更高\n if (!currentFarthest || !results.isFull() || dist < currentFarthest.distance) {\n candidates.push({ idx: neighborIdx, distance: dist });\n\n if (!results.isFull()) {\n results.push({ idx: neighborIdx, distance: dist });\n } else {\n results.replace({ idx: neighborIdx, distance: dist });\n }\n }\n }\n }\n }\n \n // MidiHeap优化 - toSortedArray 直接返回有序数组\n return results.toSortedArray().reverse();\n }\n\n /**\n * 通过启发式方法选择邻居\n * 这是保证图连接多样性、避免陷入局部最优的关键\n */\n function getNeighborsByHeuristic(candidates: Neighbor[], M: number): Neighbor[] {\n if (candidates.length <= M) {\n return candidates;\n }\n\n const result: Neighbor[] = [];\n const visited = new Set();\n\n for (const cand of candidates) {\n if (result.length >= M) break;\n if (visited.has(cand.idx)) continue;\n \n let good = true;\n for (const res of result) {\n if (distance(cand.idx, res.idx) < cand.distance) {\n good = false;\n break;\n }\n }\n\n if (good) {\n result.push(cand);\n visited.add(cand.idx);\n }\n }\n return result;\n }\n\n /**\n * 从邻居列表中移除指定节点\n */\n function removeNodeFromNeighbors(nodeIdx: number) {\n // 遍历所有节点，从它们的邻居列表中移除被删除的节点\n for (let i = 0; i < neighbors.length; i++) {\n if (!isValidNode(i)) continue; // 跳过已删除的节点\n \n const nodeNeighbors = neighbors[i];\n if (!nodeNeighbors) continue;\n \n for (let level = 0; level < nodeNeighbors.length; level++) {\n const levelNeighbors = nodeNeighbors[level];\n if (!levelNeighbors) continue;\n \n // 过滤掉被删除的节点\n nodeNeighbors[level] = levelNeighbors.filter(neighborIdx => neighborIdx !== nodeIdx);\n }\n }\n }\n\n /**\n * 重新选择入口点\n */\n function reselectEntryPoint() {\n let newEntryPoint = { idx: -1, level: -1 };\n \n // 找到层级最高的有效节点作为新入口点\n for (let i = 0; i < vectors.length; i++) {\n if (!isValidNode(i)) continue;\n \n const nodeLevel = nodeToLevels.get(i) || 0;\n if (nodeLevel > newEntryPoint.level) {\n newEntryPoint = { idx: i, level: nodeLevel };\n }\n }\n \n // 如果没有找到有效节点，重置入口点\n if (newEntryPoint.idx === -1) {\n entryPoint.idx = -1;\n entryPoint.level = -1;\n } else {\n entryPoint.idx = newEntryPoint.idx;\n entryPoint.level = newEntryPoint.level;\n }\n }\n\n /**\n * 连接新节点和它的邻居\n */\n function connectNeighbors(nodeIdx: number, level: number, nearestNeighbors: Neighbor[]) {\n // 根据层级使用正确的最大连接数：底层M*2，其他层M\n const maxConnections = level === 0 ? M * 2 : M;\n \n // 应用启发式算法选择最多样化的邻居\n const selectedNeighbors = getNeighborsByHeuristic(nearestNeighbors, maxConnections);\n neighbors[nodeIdx]![level] = selectedNeighbors.map(n => n.idx);\n\n for (const neighbor of selectedNeighbors) {\n \n if (!Array.isArray(neighbors[neighbor.idx])) {\n neighbors[neighbor.idx] = [];\n }\n if (!Array.isArray(neighbors[neighbor.idx]![level])) {\n neighbors[neighbor.idx]![level] = [];\n }\n\n neighbors[neighbor.idx]![level]!.push(nodeIdx);\n\n // MidiHeap优化 + BUG修复 - 使用层级对应的最大连接数进行修剪\n if (neighbors[neighbor.idx]![level]!.length > maxConnections) {\n const connections = neighbors[neighbor.idx]![level]!;\n \n // 使用MidiHeap选出Top-maxConnections最近的邻居\n const heap = new MidiHeapGeneric(maxConnections, (a, b) => b.distance - a.distance);\n\n for(const connIdx of connections) {\n const dist = distance(neighbor.idx, connIdx);\n const connNode = { idx: connIdx, distance: dist };\n\n if (!heap.isFull()) {\n heap.push(connNode);\n } else if (dist < heap.peek()!.distance) {\n heap.replace(connNode);\n }\n }\n neighbors[neighbor.idx]![level] = heap.toArray().map(c => c.idx);\n }\n }\n }\n\n // --- 公共 API ---\n\n function insertNode(vector: T) {\n const newNodeIdx = vectors.length;\n\n vectors.push(vector);\n neighbors.push([]);\n\n if (entryPoint.idx === -1) {\n entryPoint.idx = newNodeIdx;\n entryPoint.level = 0;\n neighbors[newNodeIdx]![0] = [];\n nodeToLevels.set(newNodeIdx, 0);\n return;\n }\n \n // 🎯 确定性层级分配函数\n function assignLevelDeterministic(arrayIndex: number, maxLevels: number): number {\n const internalId = arrayIndex + 1;\n const lowestBit = internalId & -internalId;\n const level = 31 - Math.clz32(lowestBit);\n return Math.min(level, maxLevels - 1);\n }\n\n // 确定新节点的最高层级 - 使用确定性算法\n const max_level = 16; // 与Current HNSW保持一致\n const randomLevel = assignLevelDeterministic(newNodeIdx, max_level);\n const topLevel = entryPoint.level;\n \n // 记录节点的最高层级\n nodeToLevels.set(newNodeIdx, randomLevel);\n \n let currentNodeIdx = entryPoint.idx;\n \n // 从顶层向下搜索\n for (let level = topLevel; level > randomLevel; level--) {\n const results = searchLayerWithQuery(vector, currentNodeIdx, level, 1);\n if (results.length > 0) {\n currentNodeIdx = results[0]!.idx;\n }\n }\n\n // 在每一层连接邻居\n for (let level = Math.min(randomLevel, topLevel); level >= 0; level--) {\n const nearestCandidates = searchLayerWithQuery(vector, currentNodeIdx, level, efConstruction);\n \n // 确保即使候选者少于M个也能正确处理\n const M_level = level === 0 ? M * 2 : M;\n const selectedNeighbors = getNeighborsByHeuristic(nearestCandidates, M_level);\n\n connectNeighbors(newNodeIdx, level, selectedNeighbors);\n\n // 从已选出的、最多样化的邻居中选择下一层的入口点\n if (selectedNeighbors.length > 0) {\n currentNodeIdx = selectedNeighbors[0]!.idx; \n }\n }\n \n // 如果新节点的层级更高，则更新入口点\n if (randomLevel > topLevel) {\n entryPoint.idx = newNodeIdx;\n entryPoint.level = randomLevel;\n }\n }\n\n function deleteNode(nodeIdx: number): boolean {\n // 检查节点是否存在且有效\n if (nodeIdx < 0 || nodeIdx >= vectors.length || deletedNodes.has(nodeIdx)) {\n return false; // 节点不存在或已被删除\n }\n\n // 标记节点为已删除\n deletedNodes.add(nodeIdx);\n\n // 从所有邻居的连接中移除该节点\n removeNodeFromNeighbors(nodeIdx);\n\n // 如果删除的是入口点，需要重新选择入口点\n if (nodeIdx === entryPoint.idx) {\n reselectEntryPoint();\n }\n\n return true;\n }\n\n function search(queryVector: T, k: number, efSearch?: number) {\n if (entryPoint.idx === -1 || !isValidNode(entryPoint.idx)) return [];\n\n let currentNodeIdx = entryPoint.idx;\n const topLevel = entryPoint.level;\n \n // 使用新的searchLayerWithQuery函数\n for (let level = topLevel; level > 0; level--) {\n const results = searchLayerWithQuery(queryVector, currentNodeIdx, level, 1);\n if (results.length > 0) {\n currentNodeIdx = results[0]!.idx;\n } else {\n // 如果当前层级没有找到有效结果，尝试重新选择入口点\n reselectEntryPoint();\n if (entryPoint.idx === -1) return [];\n currentNodeIdx = entryPoint.idx;\n break;\n }\n }\n \n const finalEf = Math.max(k, efSearch || efConstruction);\n const finalResults = searchLayerWithQuery(queryVector, currentNodeIdx, 0, finalEf);\n\n return finalResults.slice(0, k);\n }\n\n return {\n insertNode,\n search,\n deleteNode,\n getStats: () => ({\n nodeCount: vectors.length,\n activeNodeCount: vectors.length - deletedNodes.size,\n deletedNodeCount: deletedNodes.size,\n entryPoint: { ...entryPoint }\n }),\n };\n}\n\n/**\n * 泛型HNSW索引实现\n * \n * 🚀 主要特性：\n * 1. 完全泛型化 - 支持任意数据类型和距离函数\n * 2. 自定义距离函数 - 支持传入任意的距离计算函数\n * 3. 优化性能 - 使用MidiHeap和BinaryHeap优化\n * 4. 删除功能 - 完整的软删除支持\n * \n * 📊 适用场景：\n * - 向量相似性搜索（使用欧几里得距离、余弦距离等）\n * - 字符串相似性搜索（使用编辑距离、Jaccard距离等）\n * - 图节点相似性搜索（使用图距离函数）\n * - 任意自定义距离度量的相似性搜索\n * \n * 🎯 使用示例：\n * ```typescript\n * // 向量相似性搜索\n * const vectorIndex = createHNSWIndex({\n * M: 16,\n * efConstruction: 200,\n * distanceFunction: (a, b) => cosineDistance(a, b)\n * });\n * \n * // 字符串相似性搜索\n * const stringIndex = createHNSWIndex({\n * M: 16,\n * efConstruction: 200,\n * distanceFunction: (a, b) => editDistance(a, b)\n * });\n * ```\n */ ","import { BinaryHeapGeneric } from './binary-heap';\nimport { MidiHeapGeneric } from './midi-heap';\n\ninterface HNSWConfig {\n M: number;\n efConstruction: number;\n metricType: 'cosine' | 'l2';\n}\n\ninterface Neighbor {\n idx: number;\n distance: number;\n}\n\nexport interface HNSWIndex {\n insertNode: (vector: number[]) => void;\n search: (queryVector: number[], k: number, efSearch?: number) => Neighbor[];\n deleteNode: (nodeIdx: number) => boolean;\n getStats: () => { \n nodeCount: number; \n activeNodeCount: number;\n deletedNodeCount: number;\n entryPoint: { idx: number; level: number } \n };\n}\n\nexport function createHNSWIndex(config: HNSWConfig): HNSWIndex {\n const { M, efConstruction, metricType } = config;\n\n // --- 数据存储 ---\n // 使用最直接、最高效的数据结构\n const vectors: number[][] = [];\n const norms: number[] = [];\n const neighbors: number[][][] = []; // [nodeId][level] -> [neighborId, ...]\n const entryPoint = { idx: -1, level: -1 };\n\n // --- 删除功能相关数据结构 ---\n const deletedNodes: Set = new Set();\n const nodeToLevels: Map = new Map(); // 记录每个节点的最高层级\n\n // --- 性能优化组件 ---\n // 在构造时分配，避免重复创建\n let visited: Uint8Array | null = null;\n const maxNodes = 10000; // 预设最大容量\n visited = new Uint8Array(maxNodes);\n\n // --- 核心算法 ---\n\n /**\n * 检查节点是否有效（未被删除）\n */\n function isValidNode(idx: number): boolean {\n return idx >= 0 && idx < vectors.length && !deletedNodes.has(idx);\n }\n\n /**\n * 优化的距离计算函数 - 计算两个已存在节点之间的距离\n * - 使用预计算的范数\n * - 循环展开以提升JIT效率\n */\n function distance(idxA: number, idxB: number): number {\n // 检查节点有效性\n if (!isValidNode(idxA) || !isValidNode(idxB)) {\n return Infinity; // 无效节点返回无穷大距离\n }\n\n const vecA = vectors[idxA]!;\n const vecB = vectors[idxB]!;\n const normA = norms[idxA]!;\n const normB = norms[idxB]!;\n const len = vecA.length;\n\n if (metricType === 'l2') {\n let d = 0;\n for (let j = 0; j < len; j++) {\n const diff = vecA[j]! - vecB[j]!;\n d += diff * diff;\n }\n return d;\n }\n\n // Default to cosine distance\n let dotProduct = 0;\n let i = 0;\n\n // 循环展开 (手动SIMD)\n for (; i < len - 3; i += 4) {\n dotProduct +=\n vecA[i]! * vecB[i]! +\n vecA[i + 1]! * vecB[i + 1]! +\n vecA[i + 2]! * vecB[i + 2]! +\n vecA[i + 3]! * vecB[i + 3]!;\n }\n\n // 处理剩余部分\n for (; i < len; i++) {\n dotProduct += vecA[i]! * vecB[i]!;\n }\n \n return (normA === 0 || normB === 0) ? 1.0 : 1.0 - dotProduct / (normA * normB);\n }\n\n /**\n * 优化的距离计算函数 - 支持查询向量到目标节点\n */\n function distanceToQuery(queryVector: number[], queryNorm: number, targetIdx: number): number {\n // 检查目标节点有效性\n if (!isValidNode(targetIdx)) {\n return Infinity; // 无效节点返回无穷大距离\n }\n\n const targetVector = vectors[targetIdx]!;\n const targetNorm = norms[targetIdx]!;\n const len = queryVector.length;\n\n if (metricType === 'l2') {\n let d = 0;\n for (let j = 0; j < len; j++) {\n const diff = queryVector[j]! - targetVector[j]!;\n d += diff * diff;\n }\n return d;\n }\n\n // Default to cosine distance\n let dotProduct = 0;\n let i = 0;\n\n for (; i < len - 3; i += 4) {\n dotProduct +=\n queryVector[i]! * targetVector[i]! +\n queryVector[i + 1]! * targetVector[i + 1]! +\n queryVector[i + 2]! * targetVector[i + 2]! +\n queryVector[i + 3]! * targetVector[i + 3]!;\n }\n\n // 处理剩余部分\n for (; i < len; i++) {\n dotProduct += queryVector[i]! * targetVector[i]!;\n }\n \n return (queryNorm === 0 || targetNorm === 0) ? 1.0 : 1.0 - dotProduct / (queryNorm * targetNorm);\n }\n\n /**\n * 在指定层级上搜索最近的 ef 个邻居 - 使用优化的堆算法\n * @织: 关键优化 - 使用MidiHeap处理有界结果集，用BinaryHeap处理无界候选集\n */\n function searchLayerWithQuery(\n queryVector: number[],\n queryNorm: number,\n startNodeIdx: number,\n level: number,\n ef: number\n ): Neighbor[] {\n if (!visited) throw new Error(\"Visited set not initialized\");\n visited.fill(0);\n\n // 检查起始节点有效性\n if (!isValidNode(startNodeIdx)) {\n return []; // 如果起始节点无效，返回空结果\n }\n\n // @织: 性能优化 - 使用动态二叉堆处理大小不定的候选集\n const candidates = new BinaryHeapGeneric([], (a, b) => a.distance - b.distance);\n // @织: 性能优化 - 使用固定容量的MidiHeap处理Top-K结果集，性能更高\n const results = new MidiHeapGeneric(ef, (a, b) => b.distance - a.distance);\n\n const startNodeDist = distanceToQuery(queryVector, queryNorm, startNodeIdx);\n visited[startNodeIdx] = 1;\n candidates.push({ idx: startNodeIdx, distance: startNodeDist });\n results.push({ idx: startNodeIdx, distance: startNodeDist });\n\n while (candidates.length > 0) {\n const bestCandidate = candidates.peek()!;\n const farthestResult = results.peek();\n\n // @织: MidiHeap优化 - 剪枝逻辑更清晰\n if (farthestResult && bestCandidate.distance > farthestResult.distance && results.isFull()) {\n break; \n }\n const cand = candidates.pop()!;\n\n const nodeNeighbors = neighbors[cand.idx]?.[level] || [];\n for (const neighborIdx of nodeNeighbors) {\n // 只处理有效的邻居节点\n if (visited[neighborIdx] === 0 && isValidNode(neighborIdx)) {\n visited[neighborIdx] = 1;\n const dist = distanceToQuery(queryVector, queryNorm, neighborIdx);\n const currentFarthest = results.peek();\n \n // @织: MidiHeap优化 - 使用isFull和replace API，效率更高\n if (!currentFarthest || !results.isFull() || dist < currentFarthest.distance) {\n candidates.push({ idx: neighborIdx, distance: dist });\n\n if (!results.isFull()) {\n results.push({ idx: neighborIdx, distance: dist });\n } else {\n results.replace({ idx: neighborIdx, distance: dist });\n }\n }\n }\n }\n }\n \n // @织: MidiHeap优化 - toSortedArray 直接返回有序数组\n return results.toSortedArray().reverse();\n }\n\n /**\n * 通过启发式方法选择邻居 (来自Current版本的核心逻辑)\n * 这是保证图连接多样性、避免陷入局部最优的关键\n */\n function getNeighborsByHeuristic(candidates: Neighbor[], M: number): Neighbor[] {\n if (candidates.length <= M) {\n return candidates;\n }\n\n const result: Neighbor[] = [];\n // @织: 此处逻辑较复杂，暂时保持原样，未来可进一步优化\n const visited = new Set();\n\n for (const cand of candidates) {\n if (result.length >= M) break;\n if (visited.has(cand.idx)) continue;\n \n let good = true;\n for (const res of result) {\n if (distance(cand.idx, res.idx) < cand.distance) {\n good = false;\n break;\n }\n }\n\n if (good) {\n result.push(cand);\n visited.add(cand.idx);\n }\n }\n return result;\n }\n\n /**\n * 从邻居列表中移除指定节点\n */\n function removeNodeFromNeighbors(nodeIdx: number) {\n // 遍历所有节点，从它们的邻居列表中移除被删除的节点\n for (let i = 0; i < neighbors.length; i++) {\n if (!isValidNode(i)) continue; // 跳过已删除的节点\n \n const nodeNeighbors = neighbors[i];\n if (!nodeNeighbors) continue;\n \n for (let level = 0; level < nodeNeighbors.length; level++) {\n const levelNeighbors = nodeNeighbors[level];\n if (!levelNeighbors) continue;\n \n // 过滤掉被删除的节点\n nodeNeighbors[level] = levelNeighbors.filter(neighborIdx => neighborIdx !== nodeIdx);\n }\n }\n }\n\n /**\n * 重新选择入口点\n */\n function reselectEntryPoint() {\n let newEntryPoint = { idx: -1, level: -1 };\n \n // 找到层级最高的有效节点作为新入口点\n for (let i = 0; i < vectors.length; i++) {\n if (!isValidNode(i)) continue;\n \n const nodeLevel = nodeToLevels.get(i) || 0;\n if (nodeLevel > newEntryPoint.level) {\n newEntryPoint = { idx: i, level: nodeLevel };\n }\n }\n \n // 如果没有找到有效节点，重置入口点\n if (newEntryPoint.idx === -1) {\n entryPoint.idx = -1;\n entryPoint.level = -1;\n } else {\n entryPoint.idx = newEntryPoint.idx;\n entryPoint.level = newEntryPoint.level;\n }\n }\n\n /**\n * 连接新节点和它的邻居 - 已修复\n * @织: 性能优化 - 在邻居修剪时使用MidiHeap代替排序，性能更佳\n * @织: BUG修复 - 正确处理底层（level 0）的 M*2 连接数限制\n */\n function connectNeighbors(nodeIdx: number, level: number, nearestNeighbors: Neighbor[]) {\n // @织: BUG修复 - 根据层级使用正确的最大连接数：底层M*2，其他层M\n const maxConnections = level === 0 ? M * 2 : M;\n \n // @织: BUG修复 - 应用启发式算法选择最多样化的邻居\n const selectedNeighbors = getNeighborsByHeuristic(nearestNeighbors, maxConnections);\n neighbors[nodeIdx]![level] = selectedNeighbors.map(n => n.idx);\n\n for (const neighbor of selectedNeighbors) { // @织: BUG修复 - 只连接被选中的邻居\n \n if (!Array.isArray(neighbors[neighbor.idx])) {\n neighbors[neighbor.idx] = [];\n }\n if (!Array.isArray(neighbors[neighbor.idx]![level])) {\n neighbors[neighbor.idx]![level] = [];\n }\n\n neighbors[neighbor.idx]![level]!.push(nodeIdx);\n\n // @织: MidiHeap优化 + BUG修复 - 使用层级对应的最大连接数进行修剪\n if (neighbors[neighbor.idx]![level]!.length > maxConnections) {\n const connections = neighbors[neighbor.idx]![level]!;\n \n // 使用MidiHeap选出Top-maxConnections最近的邻居\n const heap = new MidiHeapGeneric(maxConnections, (a, b) => b.distance - a.distance);\n\n for(const connIdx of connections) {\n const dist = distance(neighbor.idx, connIdx);\n const connNode = { idx: connIdx, distance: dist };\n\n if (!heap.isFull()) {\n heap.push(connNode);\n } else if (dist < heap.peek()!.distance) {\n heap.replace(connNode);\n }\n }\n neighbors[neighbor.idx]![level] = heap.toArray().map(c => c.idx);\n }\n }\n }\n\n\n // --- 公共 API ---\n\n function insertNode(vector: number[]) {\n const newNodeIdx = vectors.length;\n let norm = 0;\n for (let i = 0; i < vector.length; i++) {\n norm += vector[i]! * vector[i]!;\n }\n norm = Math.sqrt(norm);\n\n vectors.push(vector);\n norms.push(norm);\n neighbors.push([]);\n\n if (entryPoint.idx === -1) {\n entryPoint.idx = newNodeIdx;\n entryPoint.level = 0;\n neighbors[newNodeIdx]![0] = [];\n nodeToLevels.set(newNodeIdx, 0);\n return;\n }\n \n // 🎯 确定性层级分配函数 - 与原版本完全相同\n function assignLevelDeterministic(arrayIndex: number, maxLevels: number): number {\n const internalId = arrayIndex + 1;\n const lowestBit = internalId & -internalId;\n const level = 31 - Math.clz32(lowestBit);\n return Math.min(level, maxLevels - 1);\n }\n\n // 确定新节点的最高层级 - 使用确定性算法\n const max_level = 16; // 与Current HNSW保持一致\n const randomLevel = assignLevelDeterministic(newNodeIdx, max_level);\n const topLevel = entryPoint.level;\n \n // 记录节点的最高层级\n nodeToLevels.set(newNodeIdx, randomLevel);\n \n let currentNodeIdx = entryPoint.idx;\n \n // 从顶层向下搜索\n for (let level = topLevel; level > randomLevel; level--) {\n const results = searchLayerWithQuery(vector, norm, currentNodeIdx, level, 1);\n if (results.length > 0) {\n currentNodeIdx = results[0]!.idx;\n }\n }\n\n // 在每一层连接邻居\n for (let level = Math.min(randomLevel, topLevel); level >= 0; level--) {\n const nearestCandidates = searchLayerWithQuery(vector, norm, currentNodeIdx, level, efConstruction);\n \n // @织: BUG修复 - 确保即使候选者少于M个也能正确处理\n const M_level = level === 0 ? M * 2 : M;\n const selectedNeighbors = getNeighborsByHeuristic(nearestCandidates, M_level);\n\n connectNeighbors(newNodeIdx, level, selectedNeighbors);\n\n // @织: BUG修复 - 从已选出的、最多样化的邻居中选择下一层的入口点\n if (selectedNeighbors.length > 0) {\n currentNodeIdx = selectedNeighbors[0]!.idx; \n }\n }\n \n // 如果新节点的层级更高，则更新入口点\n if (randomLevel > topLevel) {\n entryPoint.idx = newNodeIdx;\n entryPoint.level = randomLevel;\n }\n }\n\n function deleteNode(nodeIdx: number): boolean {\n // 检查节点是否存在且有效\n if (nodeIdx < 0 || nodeIdx >= vectors.length || deletedNodes.has(nodeIdx)) {\n return false; // 节点不存在或已被删除\n }\n\n // 标记节点为已删除\n deletedNodes.add(nodeIdx);\n\n // 从所有邻居的连接中移除该节点\n removeNodeFromNeighbors(nodeIdx);\n\n // 如果删除的是入口点，需要重新选择入口点\n if (nodeIdx === entryPoint.idx) {\n reselectEntryPoint();\n }\n\n return true;\n }\n\n function search(queryVector: number[], k: number, efSearch?: number) {\n // @织: BUG修复 - 避免修改全局数组，直接计算查询向量的范数\n let queryNorm = 0;\n for (let i = 0; i < queryVector.length; i++) {\n queryNorm += queryVector[i]! * queryVector[i]!;\n }\n queryNorm = Math.sqrt(queryNorm);\n\n if (entryPoint.idx === -1 || !isValidNode(entryPoint.idx)) return [];\n\n let currentNodeIdx = entryPoint.idx;\n const topLevel = entryPoint.level;\n \n // @织: 使用新的searchLayerWithQuery函数\n for (let level = topLevel; level > 0; level--) {\n const results = searchLayerWithQuery(queryVector, queryNorm, currentNodeIdx, level, 1);\n if (results.length > 0) {\n currentNodeIdx = results[0]!.idx;\n } else {\n // 如果当前层级没有找到有效结果，尝试重新选择入口点\n reselectEntryPoint();\n if (entryPoint.idx === -1) return [];\n currentNodeIdx = entryPoint.idx;\n break;\n }\n }\n \n const finalEf = Math.max(k, efSearch || efConstruction);\n const finalResults = searchLayerWithQuery(queryVector, queryNorm, currentNodeIdx, 0, finalEf);\n\n return finalResults.slice(0, k);\n }\n\n return {\n insertNode,\n search,\n deleteNode,\n getStats: () => ({\n nodeCount: vectors.length,\n activeNodeCount: vectors.length - deletedNodes.size,\n deletedNodeCount: deletedNodes.size,\n entryPoint: { ...entryPoint }\n }),\n };\n}\n\n/**\n * 性能优化总结：\n * \n * 🚀 主要优化点：\n * 1. MidiHeapGeneric 引入 - 针对HNSW中固定大小（ef, M）的集合进行终极优化。\n * 2. 混合堆策略 - 对大小不定的候选集使用BinaryHeap，对Top-K结果集使用MidiHeap，实现最佳平衡。\n * 3. 高效剪枝 - 在邻居连接阶段使用MidiHeap代替Array.sort，显著降低CPU开销。\n * 4. 删除功能 - 软删除方案，保持高性能的同时支持节点删除。\n * \n * 📊 预期性能提升：\n * - 搜索操作：显著提升，MidiHeap在小数据集上的性能优势是数量级的。\n * - 插入操作：显著提升，connectNeighbors是插入时的热点之一。\n * - 删除操作：O(1)删除状态检查，软删除避免复杂图重构。\n * \n * 🎯 适用场景：\n * - 追求极致低延迟的在线查询服务。\n * - 需要快速构建索引的离线任务。\n * - 需要支持动态删除的应用场景。\n */\n\n// 导出泛型版本\nexport * from './generic';","import { createHNSWIndex } from './vector';\r\nimport { createHNSWIndex as createHNSWIndexGeneric } from './generic';\r\nexport const hnsw = {\r\n createIndex: createHNSWIndex,\r\n createIndexGeneric: createHNSWIndexGeneric,\r\n}"]}