/**
 * SQLite Vector Index using sqlite-vec
 *
 * Provides vector similarity search using sqlite-vec extension.
 */
import type Database from "better-sqlite3";
/**
 * Vector index configuration
 */
export interface SQLiteVecConfig {
    db: Database.Database;
    tableName: string;
    dimensions: number;
}
/**
 * Vector search result
 */
export interface VectorSearchResult {
    rowId: number;
    distance: number;
}
/**
 * SQLite Vector Index
 *
 * Manages vector embeddings using sqlite-vec virtual tables
 */
export declare class SQLiteVecIndex {
    private db;
    private tableName;
    private dimensions;
    constructor(config: SQLiteVecConfig);
    /**
     * Create the virtual table if not exists
     */
    createTable(): void;
    /**
     * Insert a single embedding and return the generated rowid
     *
     * Note: sqlite-vec auto-generates rowid, we cannot specify it explicitly.
     *
     * @param embedding - The embedding vector
     * @returns The generated rowid
     */
    insert(embedding: number[]): number;
    /**
     * Insert embeddings in batch (transactional)
     *
     * Note: sqlite-vec auto-generates rowids, we cannot specify them.
     *
     * @param embeddings - Array of embedding vectors
     * @returns Array of generated rowids (in insertion order)
     */
    insertBatch(embeddings: number[][]): number[];
    /**
     * Update an existing embedding
     *
     * Note: sqlite-vec doesn't support UPDATE on virtual tables.
     * We need to DELETE and INSERT.
     *
     * @param rowId - The rowid to delete
     * @param embedding - New embedding vector
     * @returns The newly generated rowid (may be different from original)
     */
    update(rowId: number, embedding: number[]): number;
    /**
     * Delete an embedding
     */
    delete(rowId: number): void;
    /**
     * Delete embeddings in batch (transactional)
     */
    deleteBatch(rowIds: number[]): void;
    /**
     * KNN search - find similar vectors
     *
     * @param query - Query embedding
     * @param limit - Number of results
     * @returns Array of { rowId, distance }
     */
    search(query: number[], limit?: number): VectorSearchResult[];
    /**
     * Get all row IDs in the index
     */
    getAllRowIds(): number[];
    /**
     * Count vectors in the index
     */
    count(): number;
    /**
     * Clear all vectors from the index
     */
    clear(): void;
    /**
     * Drop the virtual table
     */
    drop(): void;
}
/**
 * Create vector indices for episodic and semantic memories
 */
export declare function createVectorIndices(db: Database.Database, dimensions: number): {
    episodicIndex: SQLiteVecIndex;
    semanticIndex: SQLiteVecIndex;
};
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