/**
 * PostgresVectorStore - PostgreSQL 向量存储实现（使用 pgvector 扩展）
 *
 * 使用 pgvector 扩展进行高性能向量搜索：
 * - 向量表命名：quint_vec_{hash(modelId)}
 * - 利用 <=> 运算符（余弦距离）进行近似最近邻搜索
 * - 支持 IVFFlat 和 HNSW 索引
 */
import type { Finalizable, Initializable } from '@solid/community-server';
import { VectorStore } from './VectorStore';
import type { VectorRecord, VectorSearchOptions, VectorSearchResult, VectorStoreOptions } from './types';
export declare class PostgresVectorStore extends VectorStore implements Initializable, Finalizable {
    protected readonly connectionString: string;
    /** @ignored */
    private pool;
    /** @ignored */
    private db;
    constructor(options: VectorStoreOptions);
    initialize(): Promise<void>;
    finalize(): Promise<void>;
    open(): Promise<void>;
    close(): Promise<void>;
    private ensureOpen;
    private getDb;
    protected getTableName(modelId: string): string;
    private getCountTableCandidates;
    ensureVectorTable(modelId: string): Promise<void>;
    dropVectorTable(modelId: string): Promise<void>;
    hasVectorTable(modelId: string): Promise<boolean>;
    listVectorTables(): Promise<string[]>;
    upsertVector(modelId: string, id: number, embedding: number[]): Promise<void>;
    batchUpsertVectors(modelId: string, records: {
        id: number;
        embedding: number[];
    }[]): Promise<void>;
    getVector(modelId: string, id: number): Promise<VectorRecord | null>;
    deleteVector(modelId: string, id: number): Promise<void>;
    batchDeleteVectors(modelId: string, ids: number[]): Promise<void>;
    search(modelId: string, queryEmbedding: number[], options?: VectorSearchOptions): Promise<VectorSearchResult[]>;
    countVectors(modelId: string): Promise<number>;
    getVectorIds(modelId: string, options?: {
        limit?: number;
        afterId?: number;
    }): Promise<number[]>;
    private rowToVector;
    private parseEmbedding;
}