import type { SearchResult, EntityRecord, RAGResult } from '../types/index.js';
import type { DocumentStore } from './store.js';
import type { LocalEmbedder } from './embedder.js';
/**
 * Callback type for LLM answer generation.
 * The RAG engine is LLM-agnostic — the caller provides the completion function.
 */
export type RAGCompletionFn = (prompt: string) => Promise<string>;
export interface QueryOptions {
    topK?: number;
    contextChunks?: number;
    docId?: string;
    useGraph?: boolean;
    graphHops?: number;
}
export declare class RAGQueryEngine {
    private store;
    private embedder;
    private llmComplete;
    constructor(store: DocumentStore, embedder: LocalEmbedder, llmComplete: RAGCompletionFn);
    /**
     * Answer a question using hybrid retrieval (vector + keyword + graph).
     *
     * Pipeline:
     * 1. Embed the query
     * 2. Hybrid search (vector similarity + BM25 keyword)
     * 3. Graph-augmented retrieval (optional)
     * 4. Combine and deduplicate results
     * 5. Build context from top chunks
     * 6. Generate answer with LLM
     */
    query(question: string, options?: QueryOptions): Promise<RAGResult>;
    /**
     * Simple retrieval without LLM generation — just return relevant chunks.
     * Useful for building context for external LLM calls.
     */
    retrieve(question: string, options?: QueryOptions): Promise<{
        chunks: SearchResult[];
        entities: EntityRecord[];
    }>;
}
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