David Snelling
e476d45fac
- 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.
103 lines
No EOL
2.6 KiB
TypeScript
103 lines
No EOL
2.6 KiB
TypeScript
/**
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* @soulcraft/brainy-models
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*
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* Pre-bundled TensorFlow models for maximum reliability with Brainy vector database.
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* This package provides offline access to the Universal Sentence Encoder model,
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* eliminating network dependencies and ensuring consistent performance.
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*/
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import * as tf from '@tensorflow/tfjs';
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export interface ModelMetadata {
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name: string;
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version: string;
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description: string;
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dimensions: number;
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downloadDate: string;
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source: string;
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approach: string;
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modelUrl: string;
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bundledLocally: boolean;
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reliability: string;
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}
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export interface BundledModelOptions {
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verbose?: boolean;
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preferCompressed?: boolean;
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}
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/**
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* Bundled Universal Sentence Encoder for offline use
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*/
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export declare class BundledUniversalSentenceEncoder {
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private model;
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private metadata;
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private options;
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constructor(options?: BundledModelOptions);
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/**
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* Load the bundled model from local files
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*/
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load(): Promise<void>;
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/**
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* Generate embeddings for the given texts
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*/
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embed(texts: string[]): Promise<tf.Tensor2D>;
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/**
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* Generate embeddings and return as JavaScript arrays
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*/
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embedToArrays(texts: string[]): Promise<number[][]>;
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/**
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* Get model metadata
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*/
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getMetadata(): ModelMetadata | null;
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/**
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* Check if the model is loaded
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*/
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isLoaded(): boolean;
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/**
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* Get model information
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*/
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getModelInfo(): {
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inputShape: number[];
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outputShape: number[];
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} | null;
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/**
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* Dispose of the model and free memory
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*/
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dispose(): void;
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}
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/**
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* Model compression utilities
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*/
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export declare class ModelCompressor {
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/**
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* Compress model weights using quantization
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* Note: TensorFlow.js doesn't currently support model quantization
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*/
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static quantizeModel(modelPath: string, outputPath: string, options?: {
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dtype?: 'int8' | 'int16';
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}): Promise<void>;
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/**
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* Get model size information by reading files from disk
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*/
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static getModelSize(modelPath: string): Promise<{
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totalSize: number;
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weightsSize: number;
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modelJsonSize: number;
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}>;
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}
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/**
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* Utility functions
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*/
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export declare const utils: {
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/**
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* Check if bundled models are available
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*/
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checkModelsAvailable(): boolean;
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/**
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* Get bundled models directory
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*/
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getModelsDirectory(): string;
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/**
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* List available bundled models
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*/
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listAvailableModels(): string[];
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};
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export default BundledUniversalSentenceEncoder;
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//# sourceMappingURL=index.d.ts.map
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