/**
 * Optimized HNSW (Hierarchical Navigable Small World) Index implementation
 * Extends the base HNSW implementation with support for large datasets
 * Uses product quantization for dimensionality reduction and disk-based storage when needed
 */
import { HNSWIndex } from './hnswIndex.js';
// Default configuration for the optimized HNSW index
const DEFAULT_OPTIMIZED_CONFIG = {
    M: 16,
    efConstruction: 200,
    efSearch: 50,
    ml: 16,
    memoryThreshold: 1024 * 1024 * 1024, // 1GB default threshold
    productQuantization: {
        enabled: false,
        numSubvectors: 16,
        numCentroids: 256
    },
    useDiskBasedIndex: false
};
/**
 * Product Quantization implementation
 * Reduces vector dimensionality by splitting vectors into subvectors
 * and quantizing each subvector to the nearest centroid
 */
class ProductQuantizer {
    constructor(numSubvectors = 16, numCentroids = 256) {
        this.centroids = [];
        this.subvectorSize = 0;
        this.initialized = false;
        this.dimension = 0;
        this.numSubvectors = numSubvectors;
        this.numCentroids = numCentroids;
    }
    /**
     * Initialize the product quantizer with training data
     * @param vectors Training vectors to use for learning centroids
     */
    train(vectors) {
        if (vectors.length === 0) {
            throw new Error('Cannot train product quantizer with empty vector set');
        }
        this.dimension = vectors[0].length;
        this.subvectorSize = Math.ceil(this.dimension / this.numSubvectors);
        // Initialize centroids for each subvector
        for (let i = 0; i < this.numSubvectors; i++) {
            // Extract subvectors from training data
            const subvectors = vectors.map((vector) => {
                const start = i * this.subvectorSize;
                const end = Math.min(start + this.subvectorSize, this.dimension);
                return vector.slice(start, end);
            });
            // Initialize centroids for this subvector using k-means++
            this.centroids[i] = this.kMeansPlusPlus(subvectors, this.numCentroids);
        }
        this.initialized = true;
    }
    /**
     * Quantize a vector using product quantization
     * @param vector Vector to quantize
     * @returns Array of centroid indices, one for each subvector
     */
    quantize(vector) {
        if (!this.initialized) {
            throw new Error('Product quantizer not initialized. Call train() first.');
        }
        if (vector.length !== this.dimension) {
            throw new Error(`Vector dimension mismatch: expected ${this.dimension}, got ${vector.length}`);
        }
        const codes = [];
        // Quantize each subvector
        for (let i = 0; i < this.numSubvectors; i++) {
            const start = i * this.subvectorSize;
            const end = Math.min(start + this.subvectorSize, this.dimension);
            const subvector = vector.slice(start, end);
            // Find nearest centroid
            let minDist = Number.MAX_VALUE;
            let nearestCentroidIndex = 0;
            for (let j = 0; j < this.centroids[i].length; j++) {
                const centroid = this.centroids[i][j];
                const dist = this.euclideanDistanceSquared(subvector, centroid);
                if (dist < minDist) {
                    minDist = dist;
                    nearestCentroidIndex = j;
                }
            }
            codes.push(nearestCentroidIndex);
        }
        return codes;
    }
    /**
     * Reconstruct a vector from its quantized representation
     * @param codes Array of centroid indices
     * @returns Reconstructed vector
     */
    reconstruct(codes) {
        if (!this.initialized) {
            throw new Error('Product quantizer not initialized. Call train() first.');
        }
        if (codes.length !== this.numSubvectors) {
            throw new Error(`Code length mismatch: expected ${this.numSubvectors}, got ${codes.length}`);
        }
        const reconstructed = [];
        // Reconstruct each subvector
        for (let i = 0; i < this.numSubvectors; i++) {
            const centroidIndex = codes[i];
            const centroid = this.centroids[i][centroidIndex];
            // Add centroid components to reconstructed vector
            for (const component of centroid) {
                reconstructed.push(component);
            }
        }
        // Trim to original dimension if needed
        return reconstructed.slice(0, this.dimension);
    }
    /**
     * Compute squared Euclidean distance between two vectors
     * @param a First vector
     * @param b Second vector
     * @returns Squared Euclidean distance
     */
    euclideanDistanceSquared(a, b) {
        let sum = 0;
        const length = Math.min(a.length, b.length);
        for (let i = 0; i < length; i++) {
            const diff = a[i] - b[i];
            sum += diff * diff;
        }
        return sum;
    }
    /**
     * Implement k-means++ algorithm to initialize centroids
     * @param vectors Vectors to cluster
     * @param k Number of clusters
     * @returns Array of centroids
     */
    kMeansPlusPlus(vectors, k) {
        if (vectors.length < k) {
            // If we have fewer vectors than centroids, use the vectors as centroids
            return [...vectors];
        }
        const centroids = [];
        // Choose first centroid randomly
        const firstIndex = Math.floor(Math.random() * vectors.length);
        centroids.push([...vectors[firstIndex]]);
        // Choose remaining centroids
        for (let i = 1; i < k; i++) {
            // Compute distances to nearest centroid for each vector
            const distances = vectors.map((vector) => {
                let minDist = Number.MAX_VALUE;
                for (const centroid of centroids) {
                    const dist = this.euclideanDistanceSquared(vector, centroid);
                    minDist = Math.min(minDist, dist);
                }
                return minDist;
            });
            // Compute sum of distances
            const distSum = distances.reduce((sum, dist) => sum + dist, 0);
            // Choose next centroid with probability proportional to distance
            let r = Math.random() * distSum;
            let nextIndex = 0;
            for (let j = 0; j < distances.length; j++) {
                r -= distances[j];
                if (r <= 0) {
                    nextIndex = j;
                    break;
                }
            }
            centroids.push([...vectors[nextIndex]]);
        }
        return centroids;
    }
    /**
     * Get the centroids for each subvector
     * @returns Array of centroid arrays
     */
    getCentroids() {
        return this.centroids;
    }
    /**
     * Set the centroids for each subvector
     * @param centroids Array of centroid arrays
     */
    setCentroids(centroids) {
        this.centroids = centroids;
        this.numSubvectors = centroids.length;
        this.numCentroids = centroids[0].length;
        this.initialized = true;
    }
    /**
     * Get the dimension of the vectors
     * @returns Dimension
     */
    getDimension() {
        return this.dimension;
    }
    /**
     * Set the dimension of the vectors
     * @param dimension Dimension
     */
    setDimension(dimension) {
        this.dimension = dimension;
        this.subvectorSize = Math.ceil(dimension / this.numSubvectors);
    }
}
/**
 * Optimized HNSW Index implementation
 * Extends the base HNSW implementation with support for large datasets
 * Uses product quantization for dimensionality reduction and disk-based storage when needed
 */
export class HNSWIndexOptimized extends HNSWIndex {
    constructor(config = {}, distanceFunction, storage = null) {
        // Initialize base HNSW index with standard config
        super(config, distanceFunction);
        this.productQuantizer = null;
        this.storage = null;
        this.useDiskBasedIndex = false;
        this.useProductQuantization = false;
        this.quantizedVectors = new Map();
        this.memoryUsage = 0;
        this.vectorCount = 0;
        // Thread safety for memory usage tracking
        this.memoryUpdateLock = Promise.resolve();
        // Set optimized config
        this.optimizedConfig = { ...DEFAULT_OPTIMIZED_CONFIG, ...config };
        // Set storage adapter
        this.storage = storage;
        // Initialize product quantizer if enabled
        if (this.optimizedConfig.productQuantization?.enabled) {
            this.useProductQuantization = true;
            this.productQuantizer = new ProductQuantizer(this.optimizedConfig.productQuantization.numSubvectors, this.optimizedConfig.productQuantization.numCentroids);
        }
        // Set disk-based index flag
        this.useDiskBasedIndex = this.optimizedConfig.useDiskBasedIndex || false;
    }
    /**
     * Thread-safe method to update memory usage
     * @param memoryDelta Change in memory usage (can be negative)
     * @param vectorCountDelta Change in vector count (can be negative)
     */
    async updateMemoryUsage(memoryDelta, vectorCountDelta) {
        this.memoryUpdateLock = this.memoryUpdateLock.then(async () => {
            this.memoryUsage = Math.max(0, this.memoryUsage + memoryDelta);
            this.vectorCount = Math.max(0, this.vectorCount + vectorCountDelta);
        });
        await this.memoryUpdateLock;
    }
    /**
     * Thread-safe method to get current memory usage
     * @returns Current memory usage and vector count
     */
    async getMemoryUsageAsync() {
        await this.memoryUpdateLock;
        return {
            memoryUsage: this.memoryUsage,
            vectorCount: this.vectorCount
        };
    }
    /**
     * Add a vector to the index
     * Uses product quantization if enabled and memory threshold is exceeded
     */
    async addItem(item) {
        // Check if item is defined
        if (!item) {
            throw new Error('Item is undefined or null');
        }
        const { id, vector } = item;
        // Check if vector is defined
        if (!vector) {
            throw new Error('Vector is undefined or null');
        }
        // Estimate memory usage for this vector
        const vectorMemory = vector.length * 8; // 8 bytes per number (Float64)
        const connectionsMemory = this.optimizedConfig.M * this.optimizedConfig.ml * 16; // Estimate for connections
        const totalMemory = vectorMemory + connectionsMemory;
        // Update memory usage estimate (thread-safe)
        await this.updateMemoryUsage(totalMemory, 1);
        // Check if we should switch to product quantization
        const currentMemoryUsage = await this.getMemoryUsageAsync();
        if (this.useProductQuantization &&
            currentMemoryUsage.memoryUsage > this.optimizedConfig.memoryThreshold &&
            this.productQuantizer &&
            !this.productQuantizer.getDimension()) {
            // Initialize product quantizer with existing vectors
            this.initializeProductQuantizer();
        }
        // If product quantization is active, quantize the vector
        if (this.useProductQuantization &&
            this.productQuantizer &&
            this.productQuantizer.getDimension() > 0) {
            // Quantize the vector
            const codes = this.productQuantizer.quantize(vector);
            // Store the quantized vector
            this.quantizedVectors.set(id, codes);
            // Reconstruct the vector for indexing
            const reconstructedVector = this.productQuantizer.reconstruct(codes);
            // Add the reconstructed vector to the index
            return await super.addItem({ id, vector: reconstructedVector });
        }
        // If disk-based index is active and storage is available, store the vector
        if (this.useDiskBasedIndex && this.storage) {
            // Create a noun object
            const noun = {
                id,
                vector,
                connections: new Map(),
                level: 0
            };
            // Store the noun
            this.storage.saveNoun(noun).catch((error) => {
                console.error(`Failed to save noun ${id} to storage:`, error);
            });
        }
        // Add the vector to the in-memory index
        return await super.addItem(item);
    }
    /**
     * Search for nearest neighbors
     * Uses product quantization if enabled
     */
    async search(queryVector, k = 10) {
        // Check if query vector is defined
        if (!queryVector) {
            throw new Error('Query vector is undefined or null');
        }
        // If product quantization is active, quantize the query vector
        if (this.useProductQuantization &&
            this.productQuantizer &&
            this.productQuantizer.getDimension() > 0) {
            // Quantize the query vector
            const codes = this.productQuantizer.quantize(queryVector);
            // Reconstruct the query vector
            const reconstructedVector = this.productQuantizer.reconstruct(codes);
            // Search with the reconstructed vector
            return await super.search(reconstructedVector, k);
        }
        // Otherwise, use the standard search
        return await super.search(queryVector, k);
    }
    /**
     * Remove an item from the index
     */
    removeItem(id) {
        // If product quantization is active, remove the quantized vector
        if (this.useProductQuantization) {
            this.quantizedVectors.delete(id);
        }
        // If disk-based index is active and storage is available, remove the vector from storage
        if (this.useDiskBasedIndex && this.storage) {
            this.storage.deleteNoun(id).catch((error) => {
                console.error(`Failed to delete noun ${id} from storage:`, error);
            });
        }
        // Update memory usage estimate (async operation, but don't block removal)
        this.getMemoryUsageAsync().then((currentMemoryUsage) => {
            if (currentMemoryUsage.vectorCount > 0) {
                const memoryPerVector = currentMemoryUsage.memoryUsage / currentMemoryUsage.vectorCount;
                this.updateMemoryUsage(-memoryPerVector, -1);
            }
        }).catch((error) => {
            console.error('Failed to update memory usage after removal:', error);
        });
        // Remove the item from the in-memory index
        return super.removeItem(id);
    }
    /**
     * Clear the index
     */
    async clear() {
        // Clear product quantization data
        if (this.useProductQuantization) {
            this.quantizedVectors.clear();
            this.productQuantizer = new ProductQuantizer(this.optimizedConfig.productQuantization.numSubvectors, this.optimizedConfig.productQuantization.numCentroids);
        }
        // Reset memory usage (thread-safe)
        const currentMemoryUsage = await this.getMemoryUsageAsync();
        await this.updateMemoryUsage(-currentMemoryUsage.memoryUsage, -currentMemoryUsage.vectorCount);
        // Clear the in-memory index
        super.clear();
    }
    /**
     * Initialize product quantizer with existing vectors
     */
    initializeProductQuantizer() {
        if (!this.productQuantizer) {
            return;
        }
        // Get all vectors from the index
        const nouns = super.getNouns();
        const vectors = [];
        // Extract vectors
        for (const [_, noun] of nouns) {
            vectors.push(noun.vector);
        }
        // Train the product quantizer
        if (vectors.length > 0) {
            this.productQuantizer.train(vectors);
            // Quantize all existing vectors
            for (const [id, noun] of nouns) {
                const codes = this.productQuantizer.quantize(noun.vector);
                this.quantizedVectors.set(id, codes);
            }
            console.log(`Initialized product quantizer with ${vectors.length} vectors`);
        }
    }
    /**
     * Get the product quantizer
     * @returns Product quantizer or null if not enabled
     */
    getProductQuantizer() {
        return this.productQuantizer;
    }
    /**
     * Get the optimized configuration
     * @returns Optimized configuration
     */
    getOptimizedConfig() {
        return { ...this.optimizedConfig };
    }
    /**
     * Get the estimated memory usage
     * @returns Estimated memory usage in bytes
     */
    getMemoryUsage() {
        return this.memoryUsage;
    }
    /**
     * Set the storage adapter
     * @param storage Storage adapter
     */
    setStorage(storage) {
        this.storage = storage;
    }
    /**
     * Get the storage adapter
     * @returns Storage adapter or null if not set
     */
    getStorage() {
        return this.storage;
    }
    /**
     * Set whether to use disk-based index
     * @param useDiskBasedIndex Whether to use disk-based index
     */
    setUseDiskBasedIndex(useDiskBasedIndex) {
        this.useDiskBasedIndex = useDiskBasedIndex;
    }
    /**
     * Get whether disk-based index is used
     * @returns Whether disk-based index is used
     */
    getUseDiskBasedIndex() {
        return this.useDiskBasedIndex;
    }
    /**
     * Set whether to use product quantization
     * @param useProductQuantization Whether to use product quantization
     */
    setUseProductQuantization(useProductQuantization) {
        this.useProductQuantization = useProductQuantization;
    }
    /**
     * Get whether product quantization is used
     * @returns Whether product quantization is used
     */
    getUseProductQuantization() {
        return this.useProductQuantization;
    }
}
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