chore: recovery checkpoint - v3.0 API successfully recovered
CRITICAL CHECKPOINT - DO NOT PUSH TO GITHUB Recovery Status: - Successfully recovered brainy.ts from compiled JavaScript - All core v3.0 API methods functional (add, get, update, delete, relate, find, etc.) - Neural subsystem intact (562KB embedded patterns, NLP working) - Augmentation pipeline operational (20+ augmentations) - HNSW clustering system complete - Triple Intelligence compiled (needs constructor fix) - Test suite validates functionality Changes preserved: - 898 files with changes from last 3 days - 144,475 insertions - All augmentation improvements - All test coverage enhancements - Complete v3.0 feature set This is a LOCAL checkpoint only - contains recovered work after corruption incident. Created backup in .backups/brainy-full-20250910-151314.tar.gz Branch: recovery-checkpoint-20250910-151433 Date: Wed Sep 10 03:18:04 PM PDT 2025
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.recovery-workspace/dist-backup-20250910-141917/neural/improvedNeuralAPI.d.ts
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.recovery-workspace/dist-backup-20250910-141917/neural/improvedNeuralAPI.d.ts
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/**
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* Improved Neural API - Clean, Consistent, Performant
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*
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* Public API Surface:
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* - brain.neural.similar(a, b, options?) // Similarity calculation
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* - brain.neural.clusters(items?, options?) // Semantic clustering
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* - brain.neural.neighbors(id, options?) // K-nearest neighbors
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* - brain.neural.hierarchy(id, options?) // Semantic hierarchy
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* - brain.neural.outliers(options?) // Anomaly detection
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* - brain.neural.visualize(options?) // Visualization data
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*
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* Advanced Clustering:
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* - brain.neural.clusterByDomain(field, options?) // Domain-aware clustering
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* - brain.neural.clusterByTime(field, windows, options?) // Temporal clustering
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* - brain.neural.clusterStream(options?) // AsyncIterator for streaming
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* - brain.neural.updateClusters(items, options?) // Incremental clustering
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*
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* Private methods are prefixed with _ and not exposed in public API
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*/
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import { Vector } from '../coreTypes.js';
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import { SemanticCluster, DomainCluster, TemporalCluster, EnhancedSemanticCluster, SimilarityOptions, SimilarityResult, NeighborOptions, NeighborsResult, SemanticHierarchy, HierarchyOptions, ClusteringOptions, DomainClusteringOptions, TemporalClusteringOptions, StreamClusteringOptions, VisualizationOptions, VisualizationResult, OutlierOptions, Outlier, StreamingBatch, TimeWindow, PerformanceMetrics, NeuralAPIConfig } from './types.js';
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export declare class ImprovedNeuralAPI {
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private brain;
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private config;
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private similarityCache;
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private clusterCache;
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private hierarchyCache;
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private neighborsCache;
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private performanceMetrics;
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constructor(brain: any, config?: NeuralAPIConfig);
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/**
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* Calculate similarity between any two items (auto-detection)
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* Supports: IDs, text strings, vectors, or mixed types
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*/
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similar(a: string | Vector | any, b: string | Vector | any, options?: SimilarityOptions): Promise<number | SimilarityResult>;
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/**
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* Intelligent semantic clustering with auto-routing
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* - No input: Cluster all data
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* - Array: Cluster specific items
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* - String: Find clusters near this item
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* - Options object: Advanced configuration
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*/
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clusters(input?: string | string[] | ClusteringOptions): Promise<SemanticCluster[]>;
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/**
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* Fast hierarchical clustering using HNSW levels
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*/
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clusterFast(options?: {
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level?: number;
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maxClusters?: number;
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}): Promise<SemanticCluster[]>;
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/**
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* Large-scale clustering with intelligent sampling
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*/
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clusterLarge(options?: {
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sampleSize?: number;
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strategy?: 'random' | 'diverse' | 'recent';
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}): Promise<SemanticCluster[]>;
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/**
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* Domain-aware clustering based on metadata fields
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*/
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clusterByDomain(field: string, options?: DomainClusteringOptions): Promise<DomainCluster[]>;
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/**
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* Temporal clustering based on time windows
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*/
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clusterByTime(timeField: string, windows: TimeWindow[], options?: TemporalClusteringOptions): Promise<TemporalCluster[]>;
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/**
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* Streaming clustering with real-time updates
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*/
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clusterStream(options?: StreamClusteringOptions): AsyncIterableIterator<StreamingBatch>;
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/**
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* Incremental clustering - add new items to existing clusters
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*/
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updateClusters(newItems: string[], options?: ClusteringOptions): Promise<SemanticCluster[]>;
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/**
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* Enhanced clustering with relationship analysis using verbs
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* Returns clusters with intra-cluster and inter-cluster relationship information
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*
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* Scalable for millions of nodes - uses efficient batching and filtering
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*/
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clustersWithRelationships(input?: string | string[] | ClusteringOptions, options?: {
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batchSize?: number;
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maxRelationships?: number;
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}): Promise<EnhancedSemanticCluster[]>;
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/**
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* Find K-nearest semantic neighbors
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*/
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neighbors(id: string, options?: NeighborOptions): Promise<NeighborsResult>;
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/**
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* Build semantic hierarchy around an item
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*/
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hierarchy(id: string, options?: HierarchyOptions): Promise<SemanticHierarchy>;
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/**
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* Detect outliers and anomalous items
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*/
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outliers(options?: OutlierOptions): Promise<Outlier[]>;
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/**
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* Generate visualization data for graph libraries
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*/
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visualize(options?: VisualizationOptions): Promise<VisualizationResult>;
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private _routeClusteringAlgorithm;
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private _performClustering;
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/**
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* SEMANTIC-AWARE CLUSTERING: Uses existing NounType/VerbType taxonomy + HNSW
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*/
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private _performSemanticClustering;
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/**
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* HIERARCHICAL CLUSTERING: Uses existing HNSW levels for O(n) clustering
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*/
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private _performHierarchicalClustering;
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/**
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* K-MEANS CLUSTERING: Real implementation using existing distance functions
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*/
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private _performKMeansClustering;
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/**
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* DBSCAN CLUSTERING: Density-based clustering with adaptive parameters using HNSW
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*/
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private _performDBSCANClustering;
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/**
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* GRAPH COMMUNITY DETECTION: Uses existing verb relationships for clustering
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*/
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private _performGraphClustering;
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/**
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* MULTI-MODAL FUSION: Combines vector + graph + semantic + Triple Intelligence
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*/
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private _performMultiModalClustering;
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/**
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* SAMPLED CLUSTERING: For very large datasets using intelligent sampling
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*/
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private _performSampledClustering;
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private _similarityById;
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private _similarityByVector;
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private _similarityByText;
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private _isId;
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private _isVector;
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private _convertToVector;
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private _createSimilarityKey;
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private _createClusteringKey;
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private _cacheResult;
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private _trackPerformance;
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private _createPerformanceMetrics;
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private _initializeCleanupTimer;
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/**
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* Build graph structure from existing verb relationships
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*/
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private _buildGraphFromVerbs;
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/**
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* Detect communities using Louvain modularity optimization
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*/
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private _detectCommunities;
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/**
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* Refine community boundaries using vector similarity
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*/
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private _refineCommunitiesWithVectors;
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/**
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* Get items with their metadata including noun types
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*/
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private _getItemsWithMetadata;
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/**
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* Group items by their semantic noun types
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*/
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private _groupBySemanticType;
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private _getAllItemIds;
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private _getTotalItemCount;
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private _calculateTotalWeight;
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private _getNeighborCommunities;
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private _calculateModularityGain;
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private _getNodeDegree;
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private _getEdgesToCommunity;
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private _getCommunityWeight;
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private _calculateCommunityModularity;
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private _calculateCommunityDensity;
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private _findStrongestConnections;
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/**
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* Get items with their vector representations
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*/
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private _getItemsWithVectors;
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/**
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* Calculate centroid from items using existing distance functions
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*/
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private _calculateCentroidFromItems;
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/**
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* Initialize centroids using k-means++ algorithm for better convergence
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*/
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private _initializeCentroidsKMeansPlusPlus;
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/**
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* Assign points to nearest centroids using existing distance functions
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*/
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private _assignPointsToCentroids;
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/**
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* Update centroids based on current assignments
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*/
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private _updateCentroids;
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/**
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* Calculate how much assignments have changed between iterations
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*/
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private _calculateAssignmentChangeRate;
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/**
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* Calculate cluster confidence for k-means clusters
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*/
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private _calculateKMeansClusterConfidence;
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/**
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* Estimate optimal eps parameter using k-nearest neighbor distances
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*/
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private _estimateOptimalEps;
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/**
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* Find neighbors within epsilon distance using efficient vector operations
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*/
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private _findNeighborsWithinEps;
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/**
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* Expand DBSCAN cluster by adding density-reachable points
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*/
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private _expandCluster;
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/**
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* Calculate DBSCAN cluster confidence based on density
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*/
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private _calculateDBSCANClusterConfidence;
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/**
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* Calculate squared Euclidean distance (more efficient than sqrt)
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*/
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private _calculateSquaredDistance;
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/**
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* Calculate vector coherence for community refinement
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*/
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private _calculateVectorCoherence;
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private _getItemsByField;
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/**
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* Generate intelligent cluster labels using Triple Intelligence
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*/
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private _generateIntelligentClusterLabel;
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/**
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* Generate simple cluster labels based on semantic analysis
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*/
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private _generateClusterLabel;
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/**
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* Fuse clustering results using Triple Intelligence consensus
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*/
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private _fuseClusteringResultsWithTripleIntelligence;
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/**
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* Get items in a specific cluster from cluster sets
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*/
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private _getItemsInCluster;
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/**
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* Count co-occurrences between two sets of assignments
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*/
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private _countCoOccurrences;
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/**
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* Calculate fusion confidence based on algorithm agreement
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*/
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private _calculateFusionConfidence;
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/**
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* Generate empty clustering result for edge cases
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*/
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private _createEmptyResult;
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/**
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* Get sample using specified strategy for large dataset clustering
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*/
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private _getSampleUsingStrategy;
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/**
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* Random sampling
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*/
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private _getRandomSample;
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/**
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* Diverse sampling using vector space distribution
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*/
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private _getDiverseSample;
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/**
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* Recent sampling based on creation time
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*/
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private _getRecentSample;
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/**
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* Important sampling based on connection count and metadata
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*/
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private _getImportantSample;
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/**
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* Project clusters back to full dataset using HNSW neighbors
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*/
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private _projectClustersToFullDataset;
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private _groupByDomain;
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private _calculateDomainConfidence;
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private _findCrossDomainMembers;
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private _findCrossDomainClusters;
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private _getItemsByTimeWindow;
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private _calculateTemporalMetrics;
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private _mergeOverlappingTemporalClusters;
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private _adjustThresholdAdaptively;
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private _calculateItemToClusterSimilarity;
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private _recalculateClusterCentroid;
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private _calculateSimilarity;
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private _calculateEdgeWeight;
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private _sortNeighbors;
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private _buildSemanticHierarchy;
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private _detectOutliersClusterBased;
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private _detectOutliersIsolation;
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private _detectOutliersStatistical;
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private _generateVisualizationNodes;
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private _generateVisualizationEdges;
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private _generateVisualizationClusters;
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private _applyLayoutAlgorithm;
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private _manhattanDistance;
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private _calculateConfidence;
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private _generateSimilarityExplanation;
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/**
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* Get performance metrics for monitoring
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*/
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getPerformanceMetrics(operation?: string): Map<string, PerformanceMetrics[]> | PerformanceMetrics[];
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/**
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* Clear all caches
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*/
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clearCaches(): void;
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/**
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* Get cache statistics
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*/
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getCacheStats(): Record<string, {
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size: number;
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maxSize: number;
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}>;
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/**
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* Analyze data characteristics for algorithm selection
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*/
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private _analyzeDataCharacteristics;
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/**
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* Calculate centroid for a group of items
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*/
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private _calculateGroupCentroid;
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/**
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* Cluster within semantic type using vector similarity
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*/
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private _clusterWithinSemanticType;
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/**
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* Find cross-type connections via verbs
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*/
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private _findCrossTypeConnections;
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/**
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* Merge semantic clusters based on connections
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*/
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private _mergeSemanticClusters;
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/**
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* Get optimal clustering level for HNSW
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*/
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private _getOptimalClusteringLevel;
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/**
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* Get nodes at HNSW level
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*/
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private _getHNSWLevelNodes;
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/**
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* Find cluster members using HNSW neighbors
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*/
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private _findClusterMembers;
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/**
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* Calculate hierarchical clustering confidence
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*/
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private _calculateHierarchicalConfidence;
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/**
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* Assign unassigned items to nearest clusters
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*/
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private _assignUnassignedItems;
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}
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