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/utils/embedding.d.ts

@@ -0,0 +1,108 @@
+/**
+ * Embedding functions for converting data to vectors using Transformers.js
+ * Complete rewrite to eliminate TensorFlow.js and use ONNX-based models
+ */
+import { EmbeddingFunction, EmbeddingModel, Vector } from '../coreTypes.js';
+/**
+ * Detect the best available GPU device for the current environment
+ */
+export declare function detectBestDevice(): Promise<'cpu' | 'webgpu' | 'cuda'>;
+/**
+ * Resolve device string to actual device configuration
+ */
+export declare function resolveDevice(device?: string): Promise<string>;
+/**
+ * Transformers.js Sentence Encoder embedding model
+ * Uses ONNX Runtime for fast, offline embeddings with smaller models
+ * Default model: all-MiniLM-L6-v2 (384 dimensions, ~90MB)
+ */
+export interface TransformerEmbeddingOptions {
+    /** Model name/path to use - defaults to all-MiniLM-L6-v2 */
+    model?: string;
+    /** Whether to enable verbose logging */
+    verbose?: boolean;
+    /** Custom cache directory for models */
+    cacheDir?: string;
+    /** Force local files only (no downloads) */
+    localFilesOnly?: boolean;
+    /** Model precision: 'q8' = 75% smaller quantized model, 'fp32' = full precision (default) */
+    precision?: 'fp32' | 'q8';
+    /** Device to run inference on - 'auto' detects best available */
+    device?: 'auto' | 'cpu' | 'webgpu' | 'cuda' | 'gpu';
+}
+export declare class TransformerEmbedding implements EmbeddingModel {
+    private extractor;
+    private initialized;
+    private verbose;
+    private options;
+    /**
+     * Create a new TransformerEmbedding instance
+     */
+    constructor(options?: TransformerEmbeddingOptions);
+    /**
+     * Get the default cache directory for models
+     */
+    private getDefaultCacheDir;
+    /**
+     * Check if we're running in a test environment
+     */
+    private isTestEnvironment;
+    /**
+     * Log message only if verbose mode is enabled
+     */
+    private logger;
+    /**
+     * Generate mock embeddings for unit tests
+     */
+    private getMockEmbedding;
+    /**
+     * Initialize the embedding model
+     */
+    init(): Promise<void>;
+    /**
+     * Generate embeddings for text data
+     */
+    embed(data: string | string[]): Promise<Vector>;
+    /**
+     * Dispose of the model and free resources
+     */
+    dispose(): Promise<void>;
+    /**
+     * Get the dimension of embeddings produced by this model
+     */
+    getDimension(): number;
+    /**
+     * Check if the model is initialized
+     */
+    isInitialized(): boolean;
+}
+export declare const UniversalSentenceEncoder: typeof TransformerEmbedding;
+/**
+ * Create a new embedding model instance
+ */
+export declare function createEmbeddingModel(options?: TransformerEmbeddingOptions): EmbeddingModel;
+/**
+ * Default embedding function using the unified EmbeddingManager
+ * Simple, clean, reliable - no more layers of indirection
+ */
+export declare const defaultEmbeddingFunction: EmbeddingFunction;
+/**
+ * Create an embedding function with custom options
+ * NOTE: Options are validated but the singleton EmbeddingManager is always used
+ */
+export declare function createEmbeddingFunction(options?: TransformerEmbeddingOptions): EmbeddingFunction;
+/**
+ * Batch embedding function for processing multiple texts efficiently
+ */
+export declare function batchEmbed(texts: string[], options?: TransformerEmbeddingOptions): Promise<Vector[]>;
+/**
+ * Embedding functions for specific model types
+ */
+export declare const embeddingFunctions: {
+    /** Default lightweight model (all-MiniLM-L6-v2, 384 dimensions) */
+    default: EmbeddingFunction;
+    /** Create custom embedding function */
+    create: typeof createEmbeddingFunction;
+    /** Batch processing */
+    batch: typeof batchEmbed;
+};