**feat(models): add pre-bundled Universal Sentence Encoder for offline use**

- Introduced `@soulcraft/brainy-models` package with pre-bundled TensorFlow models for enhanced offline reliability.
- Added `index.d.ts` and `index.js` allowing offline embedding workflows with the Universal Sentence Encoder model.
- Included utility scripts for model compression, size retrieval, and availability checks.
- Added `metadata.json` and `model.json` defining the Universal Sentence Encoder configuration with offline bundling.
- Ensured comprehensive model documentation, error handling, and robust logging for seamless integration.
- Supported optional model quantization placeholders for future TensorFlow.js enhancements.

**Purpose**: Enable fully offline-ready embedding workflows via pre-bundled Universal Sentence Encoder models, ensuring maximum reliability and air-gapped environment compatibility.

brainy-models-package/dist/index.d.ts

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+/**
+ * @soulcraft/brainy-models
+ *
+ * Pre-bundled TensorFlow models for maximum reliability with Brainy vector database.
+ * This package provides offline access to the Universal Sentence Encoder model,
+ * eliminating network dependencies and ensuring consistent performance.
+ */
+import * as tf from '@tensorflow/tfjs';
+export interface ModelMetadata {
+    name: string;
+    version: string;
+    description: string;
+    dimensions: number;
+    downloadDate: string;
+    source: string;
+    approach: string;
+    modelUrl: string;
+    bundledLocally: boolean;
+    reliability: string;
+}
+export interface BundledModelOptions {
+    verbose?: boolean;
+    preferCompressed?: boolean;
+}
+/**
+ * Bundled Universal Sentence Encoder for offline use
+ */
+export declare class BundledUniversalSentenceEncoder {
+    private model;
+    private metadata;
+    private options;
+    constructor(options?: BundledModelOptions);
+    /**
+     * Load the bundled model from local files
+     */
+    load(): Promise<void>;
+    /**
+     * Generate embeddings for the given texts
+     */
+    embed(texts: string[]): Promise<tf.Tensor2D>;
+    /**
+     * Generate embeddings and return as JavaScript arrays
+     */
+    embedToArrays(texts: string[]): Promise<number[][]>;
+    /**
+     * Get model metadata
+     */
+    getMetadata(): ModelMetadata | null;
+    /**
+     * Check if the model is loaded
+     */
+    isLoaded(): boolean;
+    /**
+     * Get model information
+     */
+    getModelInfo(): {
+        inputShape: number[];
+        outputShape: number[];
+    } | null;
+    /**
+     * Dispose of the model and free memory
+     */
+    dispose(): void;
+}
+/**
+ * Model compression utilities
+ */
+export declare class ModelCompressor {
+    /**
+     * Compress model weights using quantization
+     * Note: TensorFlow.js doesn't currently support model quantization
+     */
+    static quantizeModel(modelPath: string, outputPath: string, options?: {
+        dtype?: 'int8' | 'int16';
+    }): Promise<void>;
+    /**
+     * Get model size information by reading files from disk
+     */
+    static getModelSize(modelPath: string): Promise<{
+        totalSize: number;
+        weightsSize: number;
+        modelJsonSize: number;
+    }>;
+}
+/**
+ * Utility functions
+ */
+export declare const utils: {
+    /**
+     * Check if bundled models are available
+     */
+    checkModelsAvailable(): boolean;
+    /**
+     * Get bundled models directory
+     */
+    getModelsDirectory(): string;
+    /**
+     * List available bundled models
+     */
+    listAvailableModels(): string[];
+};
+export default BundledUniversalSentenceEncoder;
+//# sourceMappingURL=index.d.ts.map