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

.recovery-workspace/dist-backup-20250910-141917/hnsw/partitionedHNSWIndex.d.ts

@@ -0,0 +1,101 @@
+/**
+ * Partitioned HNSW Index for Large-Scale Vector Search
+ * Implements sharding strategies to handle millions of vectors efficiently
+ */
+import { DistanceFunction, HNSWConfig, Vector, VectorDocument } from '../coreTypes.js';
+export interface PartitionConfig {
+    maxNodesPerPartition: number;
+    partitionStrategy: 'semantic' | 'hash';
+    semanticClusters?: number;
+    autoTuneSemanticClusters?: boolean;
+}
+export interface PartitionMetadata {
+    id: string;
+    nodeCount: number;
+    bounds?: {
+        centroid: Vector;
+        radius: number;
+    };
+    strategy: string;
+    created: Date;
+}
+/**
+ * Partitioned HNSW Index that splits large datasets across multiple smaller indices
+ * This enables efficient search across millions of vectors by reducing memory usage
+ * and parallelizing search operations
+ */
+export declare class PartitionedHNSWIndex {
+    private partitions;
+    private partitionMetadata;
+    private config;
+    private hnswConfig;
+    private distanceFunction;
+    private dimension;
+    private nextPartitionId;
+    constructor(partitionConfig?: Partial<PartitionConfig>, hnswConfig?: Partial<HNSWConfig>, distanceFunction?: DistanceFunction);
+    /**
+     * Add a vector to the partitioned index
+     */
+    addItem(item: VectorDocument): Promise<string>;
+    /**
+     * Search across all partitions for nearest neighbors
+     */
+    search(queryVector: Vector, k?: number, searchScope?: {
+        partitionIds?: string[];
+        maxPartitions?: number;
+    }): Promise<Array<[string, number]>>;
+    /**
+     * Select the appropriate partition for a new item
+     * Automatically chooses semantic partitioning when beneficial, falls back to hash
+     */
+    private selectPartition;
+    /**
+     * Hash-based partitioning for even distribution
+     */
+    private hashPartition;
+    /**
+     * Semantic clustering partitioning
+     */
+    private semanticPartition;
+    /**
+     * Auto-tune semantic clusters based on dataset size and performance
+     */
+    private autoTuneSemanticClusters;
+    /**
+     * Select which partitions to search based on query
+     */
+    private selectSearchPartitions;
+    /**
+     * Update partition bounds for semantic clustering
+     */
+    private updatePartitionBounds;
+    /**
+     * Split an overgrown partition into smaller partitions
+     */
+    private splitPartition;
+    /**
+     * Simple hash function for consistent partitioning
+     */
+    private simpleHash;
+    /**
+     * Get partition statistics
+     */
+    getPartitionStats(): {
+        totalPartitions: number;
+        totalNodes: number;
+        averageNodesPerPartition: number;
+        partitionDetails: PartitionMetadata[];
+    };
+    /**
+     * Remove an item from the index
+     */
+    removeItem(id: string): Promise<boolean>;
+    /**
+     * Clear all partitions
+     */
+    clear(): void;
+    /**
+     * Get total size across all partitions
+     */
+    size(): number;
+}