crewai-ts/knowledge/embedder/FastEmbedder.d.ts

TypeScript port of crewAI for agent-based workflows

/**
 * FastEmbedder Implementation
 *
 * Lightweight, efficient local embeddings using fastembed
 * Optimized for performance and low resource usage
 */
import { BaseEmbedder, BaseEmbedderOptions } from './BaseEmbedder.js';
/**
 * FastEmbedder Options
 */
export interface FastEmbedderOptions extends BaseEmbedderOptions {
    /**
     * Model name
     */
    model: string;
    /**
     * Model path
     */
    modelPath: string;
    /**
     * Dimensions of the embeddings
     */
    dimensions: number;
    /**
     * Maximum sequence length
     */
    maxLength?: number;
    /**
     * Whether to use average pooling
     */
    useAveragePooling?: boolean;
    /**
     * Request timeout in milliseconds
     */
    timeout?: number;
    /**
     * Path to the model cache directory
     * @default 'node_modules/.cache/fastembed'
     */
    cacheDir?: string;
    /**
     * Use multilingual model
     * @default false
     */
    multilingual?: boolean;
    /**
     * Whether to batch inputs internally
     * @default true
     */
    useBatching?: boolean;
    /**
     * Internal batch size for processing
     * @default 32
     */
    internalBatchSize?: number;
}
/**
 * Let TypeScript know about optional dependencies
 */
declare global {
    var FastEmbed: any;
}
/**
 * FastEmbedder
 *
 * Efficient local embeddings with minimal resource usage
 * Uses fastembed library for optimized performance
 */
export declare class FastEmbedder extends BaseEmbedder<FastEmbedderOptions> {
    /**
     * FastEmbed model instance (lazy loaded)
     */
    private model;
    /**
     * Whether the model is ready
     */
    private modelReady;
    /**
     * Model initialization promise (for concurrent calls)
     */
    private initializationPromise;
    /**
     * Whether to use multilingual model
     */
    private multilingual;
    /**
     * Path to model cache directory
     */
    private cacheDir;
    /**
     * Maximum text length
     */
    private maxLength;
    /**
     * Whether to use internal batching
     */
    private useBatching;
    /**
     * Internal batch size
     */
    private internalBatchSize;
    /**
     * Model path
     */
    protected _modelPath: string;
    /**
     * Dimensions of the embeddings
     */
    protected _dimensions: number;
    /**
     * Maximum sequence length
     */
    protected _maxLength: number;
    /**
     * Whether to use average pooling
     */
    protected _useAveragePooling: boolean;
    /**
     * Request timeout in milliseconds
     */
    protected _timeout: number;
    /**
     * Model instance
     */
    protected _modelInstance: any;
    /**
     * Constructor for FastEmbedder
     */
    constructor(options: FastEmbedderOptions);
    /**
     * Internal initialization logic
     */
    private _initializeModel;
    embed(text: string): Promise<Float32Array>;
    embedBatch(texts: string[]): Promise<Float32Array[]>;
    protected executeWithRetry<T>(operation: () => Promise<T>, maxRetries?: number, initialBackoff?: number, maxBackoff?: number): Promise<T>;
    protected executeBatchWithRetry<T>(operation: () => Promise<T>, maxRetries?: number, initialBackoff?: number, maxBackoff?: number): Promise<T>;
    protected isTransientError(error: Error): boolean;
    protected embedText(text: string): Promise<Float32Array>;
    private initializeModel;
}
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