@ooples/token-optimizer-mcp/dist/core/FoundationModelEmbeddingGenerator.d.ts

Intelligent context window optimization for Claude Code - store content externally via caching and compression, freeing up your context window for what matters

import { IEmbeddingGenerator } from '../interfaces/IEmbeddingGenerator.js';
/**
 * A simple embedding generator using hashing and statistical features
 * This is an MVP implementation that doesn't require external API calls.
 * Can be extended later to use OpenAI, HuggingFace, or other embedding APIs.
 */
export declare class FoundationModelEmbeddingGenerator implements IEmbeddingGenerator {
    private readonly dimensions;
    /**
     * @param dimensions - The dimensionality of the embedding vectors (default: 128)
     */
    constructor(dimensions?: number);
    /**
     * Generate an embedding vector for the given text
     * Uses a hybrid approach combining:
     * 1. Hashing-based features for content similarity
     * 2. Statistical features (length, character distribution)
     * 3. N-gram features for semantic similarity
     */
    generateEmbedding(text: string): Promise<number[]>;
    getDimensions(): number;
    /**
     * Normalize text for consistent embedding generation
     */
    private normalizeText;
    /**
     * Compute statistical features from text
     */
    private computeStatistics;
    /**
     * Compute n-gram features for semantic similarity
     */
    private computeNgramFeatures;
    /**
     * Normalize a vector to unit length (L2 normalization)
     */
    private normalizeVector;
}
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