/** * Triple Intelligence Engine * Revolutionary unified search combining Vector + Graph + Field intelligence * * This is Brainy's killer feature - no other database can do this! */ import { Vector, SearchResult } from '../coreTypes.js'; import type { Brainy } from '../brainy.js'; export interface TripleQuery { like?: string | Vector | any; similar?: string | Vector | any; connected?: { to?: string | string[]; from?: string | string[]; type?: string | string[]; depth?: number; maxDepth?: number; direction?: 'in' | 'out' | 'both'; }; where?: Record; limit?: number; offset?: number; mode?: 'auto' | 'vector' | 'graph' | 'metadata' | 'fusion'; boost?: 'recent' | 'popular' | 'verified' | string; explain?: boolean; threshold?: number; } export interface TripleResult extends SearchResult { vectorScore?: number; graphScore?: number; fieldScore?: number; fusionScore: number; explanation?: { plan: string; timing: Record; boosts: string[]; }; } export interface QueryPlan { startWith: 'vector' | 'graph' | 'field'; canParallelize: boolean; estimatedCost: number; steps: QueryStep[]; } export interface QueryStep { type: 'vector' | 'graph' | 'field' | 'fusion'; operation: string; estimated: number; } /** * The Triple Intelligence Engine * Unifies vector, graph, and field search into one beautiful API */ export declare class TripleIntelligenceEngine { private brain; private api; private planCache; constructor(brain: Brainy); /** * The magic happens here - one query to rule them all */ find(query: TripleQuery): Promise; /** * Generate optimal execution plan based on query shape and statistics */ private optimizeQuery; /** * Calculate real costs for each operation based on statistics */ private calculateOperationCosts; /** * Estimate selectivity of field filters */ private estimateFieldSelectivity; /** * Build optimal execution plan based on costs */ private buildOptimalPlan; /** * Build progressive execution steps */ private buildProgressiveSteps; /** * Build parallel execution steps */ private buildParallelSteps; /** * Execute searches in parallel for maximum speed */ private parallelSearch; /** * Progressive filtering for efficiency */ private progressiveSearch; /** * Vector similarity search */ private vectorSearch; /** * Graph traversal */ private graphTraversal; /** * Field-based filtering using MetadataIndex for O(log n) performance * NO FALLBACKS - Requires proper where clause and MetadataIndex */ private fieldFilter; /** * Execute a single signal query directly */ private executeSingleSignal; /** * Expand graph connections from existing candidates */ private graphExpand; /** * Vector search within existing candidates */ private vectorSearchWithin; /** * Apply field filter to existing candidates */ private applyFieldFilter; /** * Check if metadata matches filter conditions */ private matchesFilter; /** * Calculate cosine similarity between two vectors */ private cosineSimilarity; /** * Fusion ranking using Reciprocal Rank Fusion (RRF) * This is the same algorithm used by Google and Elasticsearch */ private fusionRank; /** * Calculate dynamic signal weights based on query characteristics */ private calculateSignalWeights; /** * Apply boost strategies */ private applyBoosts; /** * Add query explanations for debugging */ private addExplanations; /** * Optimize plan based on historical patterns */ /** * Clear query optimization cache */ clearCache(): void; /** * Get optimization statistics */ getStats(): any; } export declare function find(brain: Brainy, query: TripleQuery): Promise;