/**
 * Semantic Join Executor
 *
 * Performs joins based on vector similarity (semantic matching).
 * Uses embeddings to find similar items across data sources.
 */
import { VectorDatabaseService } from '../../../vector/vector-database.service';
import { IJoinClause, IJoinResult } from '../../types';
/**
 * Options for semantic join
 */
export interface ISemanticJoinOptions {
    /** Environment */
    env: string;
    /** Product */
    product: string;
    /** Embedding function for text fields */
    embedFn?: (text: string) => Promise<number[]>;
}
/**
 * Semantic match result
 */
export interface ISemanticMatch {
    /** Source row */
    sourceRow: Record<string, any>;
    /** Matched vector row */
    matchedRow: Record<string, any>;
    /** Similarity score */
    score: number;
}
/**
 * Semantic Join Executor
 *
 * Joins data based on vector similarity rather than exact key matches.
 */
export declare class SemanticJoinExecutor {
    private readonly vectorService;
    constructor(vectorService: VectorDatabaseService);
    /**
     * Execute a semantic join
     *
     * For each row in the left dataset:
     * 1. Extract or compute embedding from the embed field
     * 2. Query vector store for similar items
     * 3. Join matching items based on similarity threshold
     */
    execute<T = Record<string, any>>(left: Record<string, any>[], join: IJoinClause, leftAlias: string, options: ISemanticJoinOptions): Promise<IJoinResult<T>>;
    /**
     * Find semantic matches for a single row
     */
    private findSemanticMatches;
    /**
     * Execute batch semantic join (more efficient for large datasets)
     */
    executeBatch<T = Record<string, any>>(left: Record<string, any>[], join: IJoinClause, leftAlias: string, options: ISemanticJoinOptions, batchSize?: number): Promise<IJoinResult<T>>;
    /**
     * Get nested value from object using dot notation
     */
    private getNestedValue;
    /**
     * Merge rows with similarity score
     */
    private mergeRows;
}