semantic-ds-toolkit/dist/src/optimization/vector-compression.d.ts

Performance-first semantic layer for modern data stacks - Stable Column Anchors & intelligent inference

interface CompressedVector {
    codes: Uint8Array;
    metadata: {
        originalDimension: number;
        subspaceCount: number;
        compressionRatio: number;
    };
}
interface PQConfig {
    subspaceCount: number;
    bitsPerCode: number;
    maxIterations: number;
    convergenceThreshold: number;
    initializationMethod: 'random' | 'kmeans++';
}
export declare class ProductQuantizer {
    private config;
    private codebooks;
    private isTrained;
    constructor(config?: Partial<PQConfig>);
    train(vectors: Float32Array[]): Promise<void>;
    private splitVectorsIntoSubspaces;
    private trainSubspaceCodebook;
    private initializeCentroids;
    private kMeansPlusPlusInit;
    private computeMean;
    private randomVector;
    private euclideanDistance;
    compress(vector: Float32Array): CompressedVector;
    decompress(compressed: CompressedVector): Float32Array;
    compressBatch(vectors: Float32Array[]): CompressedVector[];
    approximateDistance(compressed1: CompressedVector, compressed2: CompressedVector): number;
    buildDistanceTables(query: Float32Array): Float32Array[];
    searchApproximate(distanceTables: Float32Array[], compressed: CompressedVector[], k?: number): Array<{
        index: number;
        distance: number;
    }>;
    getCompressionStats(): {
        subspaceCount: number;
        bitsPerCode: number;
        totalCentroids: number;
        memoryReduction: number;
        isTrained: boolean;
    };
    serialize(): string;
    deserialize(serialized: string): void;
}
export declare class ColumnFingerprintCompressor {
    private pq;
    private compressedFingerprints;
    constructor(subspaceCount?: number);
    trainOnFingerprints(fingerprints: Map<string, Float32Array>): Promise<void>;
    compressFingerprint(id: string, fingerprint: Float32Array): CompressedVector;
    findSimilarFingerprints(queryFingerprint: Float32Array, threshold?: number, maxResults?: number): Array<{
        id: string;
        similarity: number;
    }>;
    getCompressionRatio(): number;
    clear(): void;
}
export declare const globalProductQuantizer: ProductQuantizer;
export declare const globalFingerprintCompressor: ColumnFingerprintCompressor;
export {};
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