/**
 * Vector store interface and implementations for Agentis memory
 */
/**
 * Configuration for vector stores
 */
export interface VectorStoreConfig {
    dimensions: number;
    namespace?: string;
    index?: string;
}
/**
 * A vector is an array of numbers representing embeddings
 */
export type Vector = number[];
/**
 * Document with vector embedding
 */
export interface VectorDocument {
    id: string;
    vector: Vector;
    metadata: Record<string, any>;
    text?: string;
}
/**
 * Interface for vector stores
 */
export interface VectorStoreInterface {
    /**
     * Stores a vector in the database
     *
     * @param document - The document to store
     * @returns Promise resolving to success indicator
     */
    storeVector(document: VectorDocument): Promise<boolean>;
    /**
     * Searches for vectors similar to the query vector
     *
     * @param queryVector - The vector to search for
     * @param limit - Maximum number of results
     * @param filter - Optional filter to apply to the search
     * @returns Promise resolving to array of similar documents
     */
    searchVector(queryVector: Vector, limit?: number, filter?: Record<string, any>): Promise<VectorDocument[]>;
    /**
     * Deletes a vector from the database
     *
     * @param id - ID of the vector to delete
     * @returns Promise resolving to success indicator
     */
    deleteVector(id: string): Promise<boolean>;
    /**
     * Initializes the vector store
     *
     * @returns Promise resolving when initialization is complete
     */
    initialize(): Promise<void>;
}
/**
 * In-memory vector store for testing and demonstration
 */
export declare class InMemoryVectorStore implements VectorStoreInterface {
    private vectors;
    private config;
    /**
     * Creates an in-memory vector store
     *
     * @param config - Configuration options
     */
    constructor(config: VectorStoreConfig);
    /**
     * Initializes the vector store (no-op for in-memory store)
     */
    initialize(): Promise<void>;
    /**
     * Stores a vector in memory
     *
     * @param document - The document to store
     * @returns Promise resolving to success indicator
     */
    storeVector(document: VectorDocument): Promise<boolean>;
    /**
     * Calculates cosine similarity between two vectors
     *
     * @param vectorA - First vector
     * @param vectorB - Second vector
     * @returns Cosine similarity score (1 = identical, 0 = orthogonal, -1 = opposite)
     */
    private cosineSimilarity;
    /**
     * Searches for vectors similar to the query vector
     *
     * @param queryVector - The vector to search for
     * @param limit - Maximum number of results
     * @param filter - Optional filter to apply to the search
     * @returns Promise resolving to array of similar documents
     */
    searchVector(queryVector: Vector, limit?: number, filter?: Record<string, any>): Promise<VectorDocument[]>;
    /**
     * Deletes a vector from memory
     *
     * @param id - ID of the vector to delete
     * @returns Promise resolving to success indicator
     */
    deleteVector(id: string): Promise<boolean>;
}