/**
 * Vector operations for semantic search.
 *
 * Pure JS implementations — no native dependencies or SQLite UDFs needed.
 * Vectors are computed and compared in JS, stored as BLOBs in SQLite.
 */
/**
 * Compute cosine similarity between two normalized vectors.
 *
 * For normalized vectors, cosine similarity is simply the dot product:
 *   cos(a, b) = Σ a[i] * b[i]
 *
 * Returns a value in [-1, 1] where 1 = identical, 0 = orthogonal, -1 = opposite.
 */
export declare function cosineSimilarity(a: Float32Array, b: Float32Array): number;
/**
 * Pack a Float32Array into a Buffer for SQLite BLOB storage.
 *
 * Creates a copy to ensure the buffer isn't shared with other typed arrays.
 */
export declare function packVector(vector: Float32Array): Buffer;
/**
 * Unpack a BLOB (Uint8Array from node:sqlite) back to a Float32Array.
 *
 * The returned array shares the underlying buffer with the input for
 * zero-copy performance. Callers should not mutate the input after calling.
 */
export declare function unpackVector(blob: Uint8Array): Float32Array;
/**
 * Find the top-K most similar vectors from a set.
 *
 * Computes cosine similarity between the query vector and every candidate,
 * then returns the K highest-scoring results sorted by descending score.
 *
 * Uses a simple full scan — suitable for the index sizes we expect in a
 * single-project codebase (typically <100K chunks). For millions of vectors,
 * an approximate nearest neighbor index (HNSW, IVF) would be needed.
 */
export declare function topKSimilar(query: Float32Array, vectors: Map<number, Float32Array>, k: number): Array<{
    id: number;
    score: number;
}>;
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