import { type EmbeddingProvider, type VectorDocument, type VectorSearchOptions, type VectorSearchResult, type VectorStore } from '../hallucination/retrieval.js';
import { type PostgresLikeClient } from './client.js';
/** Options for the pgvector store. */
export interface PostgresVectorStoreOptions {
    /** Width of every vector. Must match the embedding function's output. */
    dimensions: number;
    /** Embeds chunks that arrive without a vector, and search queries. Defaults to hashed term vectors. */
    embed?: EmbeddingProvider;
    /** Table name, optionally schema-qualified. Defaults to `nexus_vectors`. */
    table?: string;
    /**
     * The approximate index created by `migrate()`. `hnsw` (the default) answers a search without
     * scanning every row, at the cost of slower writes; `none` scans exactly, which suits a few
     * thousand chunks.
     */
    index?: 'hnsw' | 'none';
}
/** The schema, as statements: the pgvector extension, the table, and its index. */
export declare function vectorStoreMigration(options: Omit<PostgresVectorStoreOptions, 'embed'>): string[];
/**
 * Retrieval chunks in Postgres with pgvector, ranked by cosine similarity in the database.
 *
 * The same contract as `MemoryVectorStore`, checked by the same tests. Only the nearest chunks leave
 * the database, so a search costs the same whether the table holds a thousand chunks or ten million.
 * Adding a chunk whose id exists replaces it, and a metadata filter runs in SQL.
 */
export declare class PostgresVectorStore implements VectorStore {
    private readonly client;
    private readonly options;
    private readonly table;
    private readonly embed;
    constructor(client: PostgresLikeClient, options: PostgresVectorStoreOptions);
    /** Creates the extension, table, and index. Never runs implicitly. */
    migrate(): Promise<void>;
    /** Adds chunks, or replaces those whose id exists, embedding those without a vector in one batch. */
    add(documents: VectorDocument[]): Promise<void>;
    /** The chunks most similar to a query, best first. */
    search(query: string, options?: VectorSearchOptions): Promise<VectorSearchResult[]>;
    /** The chunks most similar to a vector, best first. */
    searchVector(vector: number[], options?: VectorSearchOptions): Promise<VectorSearchResult[]>;
    /** Removes chunks by id. */
    delete(ids: readonly string[]): Promise<void>;
    /** Chunks stored. */
    size(): Promise<number>;
    private assertWidth;
}