import type { ExtractedEntity, EntityExtractionResult } from '../types/index.js';
import type { DocumentStore } from './store.js';
/**
 * Extract entities and relationships from a chunk of text.
 * Uses a structured prompt that works with any LLM provider.
 *
 * The caller is responsible for sending this prompt to their LLM
 * and passing the response back through parseExtractionResponse().
 */
export declare function buildEntityExtractionPrompt(chunkText: string, docContext?: string): string;
/**
 * Parse the LLM's JSON response into structured entities and relationships.
 * Handles common LLM output quirks (markdown fences, trailing commas, etc.).
 */
export declare function parseExtractionResponse(response: string): EntityExtractionResult;
/**
 * Store extracted entities and relationships into the DocumentStore.
 * Handles deduplication by upserting entities and resolving names to IDs.
 */
export declare function storeExtraction(store: DocumentStore, extraction: EntityExtractionResult, docId: string, chunkId: string): {
    entitiesStored: number;
    relationshipsStored: number;
};
/**
 * Deduplicate entities by fuzzy name matching.
 * Simple approach: normalize and compare lowercase names.
 * More advanced: Levenshtein distance or LLM-assisted resolution.
 */
export declare function deduplicateEntities(entities: ExtractedEntity[]): ExtractedEntity[];
//# sourceMappingURL=knowledge-graph.d.ts.map