il2cpp-dump-analyzer-mcp/dist/embeddings/simple-embeddings.d.ts

Agentic RAG system for analyzing IL2CPP dump.cs files from Unity games

import { Embeddings } from '@langchain/core/embeddings';
/**
 * A simple embedding implementation that uses TF-IDF like approach
 * with cosine similarity for retrieval.
 *
 * This is a lightweight alternative that doesn't require external models
 * or native dependencies.
 */
export declare class SimpleEmbeddings extends Embeddings {
    private vocabulary;
    private documentVectors;
    private dimensions;
    constructor();
    /**
     * Create embeddings for an array of texts
     * @param texts Array of texts to embed
     * @returns Promise resolving to a 2D array of embeddings
     */
    embedDocuments(texts: string[]): Promise<number[][]>;
    /**
     * Create an embedding for a single query text
     * @param text Text to embed
     * @returns Promise resolving to an embedding vector
     */
    embedQuery(text: string): Promise<number[]>;
    /**
     * Get the dimensionality of the embeddings
     * @returns The number of dimensions in the embedding vectors
     */
    getDimension(): number;
    /**
     * Find the most similar documents to a query
     * @param query Query text
     * @param k Number of results to return
     * @returns Array of document IDs and similarity scores
     */
    findSimilarDocuments(query: string, k?: number): Promise<Array<[string, number]>>;
    /**
     * Build vocabulary from texts
     * @param texts Array of texts
     */
    private buildVocabulary;
    /**
     * Convert text to vector
     * @param text Input text
     * @returns Vector representation
     */
    private textToVector;
    /**
     * Tokenize text into words
     * @param text Input text
     * @returns Array of tokens
     */
    private tokenize;
    /**
     * Get document frequency for a word
     * @param word Word to check
     * @returns Number of documents containing the word
     */
    private getDocumentFrequency;
    /**
     * Generate a unique ID for a document
     * @param text Document text
     * @returns Unique ID
     */
    private getDocumentId;
    /**
     * Convert sparse vector to fixed-size vector using hashing
     * @param sparseVector Sparse vector
     * @returns Fixed-size vector
     */
    private hashVector;
}