@codai/memorai-core/dist/search/SemanticSearchEngine.d.ts

Simplified advanced memory engine - no tiers, just powerful semantic search with persistence

/**
 * Advanced Semantic Search Engine for Memorai
 * Provides fuzzy matching, semantic similarity, and intelligent query understanding
 */
import type { MemoryMetadata, MemoryResult } from '../types/index.js';
import { EmbeddingService } from '../embedding/EmbeddingService.js';
export interface SemanticSearchOptions {
    enableFuzzyMatching?: boolean;
    fuzzyThreshold?: number;
    enableSemanticExpansion?: boolean;
    enableTypoTolerance?: boolean;
    weightFactors?: {
        semantic: number;
        fuzzy: number;
        recency: number;
        frequency: number;
        importance: number;
    };
    contextWindow?: number;
    diversityFactor?: number;
    limit?: number;
}
export interface SearchContext {
    recentQueries: string[];
    userPreferences: Record<string, unknown>;
    sessionContext: string[];
    timeContext: {
        timeOfDay: 'morning' | 'afternoon' | 'evening' | 'night';
        dayOfWeek: string;
        season: string;
    };
}
export interface EnhancedMemoryResult extends MemoryResult {
    searchScore: number;
    fuzzyScore: number;
    semanticScore: number;
    recencyScore: number;
    frequencyScore: number;
    contextRelevance: number;
    explanation: string;
    relatedConcepts: string[];
}
/**
 * Advanced semantic search with fuzzy matching and intelligent ranking
 */
export declare class SemanticSearchEngine {
    private embeddingService;
    private queryCache;
    private conceptCache;
    constructor(embeddingService: EmbeddingService);
    /**
     * Perform advanced semantic search with multiple ranking factors
     */
    search(query: string, memories: MemoryMetadata[], options?: SemanticSearchOptions, context?: SearchContext): Promise<EnhancedMemoryResult[]>;
    /**
     * Preprocess query to handle typos and normalize text
     */
    private preprocessQuery;
    /**
     * Get or generate embedding for query with caching
     */ private getQueryEmbedding;
    /**
     * Expand query with semantically related concepts
     */
    private expandQuery;
    /**
     * Score a single memory against the query
     */
    private scoreMemory;
    /**
     * Calculate semantic similarity score using embeddings
     */
    private calculateSemanticScore;
    /**
     * Calculate fuzzy matching score
     */
    private calculateFuzzyScore;
    /**
     * Calculate recency score (more recent = higher score)
     */
    private calculateRecencyScore;
    /**
     * Calculate frequency score based on access count
     */
    private calculateFrequencyScore;
    /**
     * Calculate context relevance score
     */
    private calculateContextRelevance;
    /**
     * Apply diversity ranking to avoid too similar results
     */
    private applyDiversityRanking;
    private cosineSimilarity;
    private fuzzyMatch;
    private jaroWinklerDistance;
    private correctTypos;
    private generateRelatedConcepts;
    private generateExplanation;
    private extractRelatedConcepts;
    private checkPreferenceMatch;
    private calculateTimeRelevance;
    private getTimeOfDayScore;
    private calculateContentSimilarity;
}
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