@gaiaverse/semantic-turning-point-detector/dist/tokensUtil.d.ts

Detects key semantic turning points in conversations using recursive semantic distance analysis. Ideal for conversation analysis, dialogue segmentation, insight detection, and AI-assisted reasoning tasks.

import { LRUCache } from 'lru-cache';
/**
 * Count tokens in a given string using OpenAI-compatible tokenization.
 *
 * @param text - The text to tokenize
 * @param modelName - Optional model name to specify the tokenizer variant, though not used as the default is sufficient.
 * @returns Number of tokens
 */
export declare function countTokens(text: string): number;
/**
 * Generates an embedding for a given text using the OpenAI API
 * This provides the vector representation for semantic distance calculation
 */
export declare function generateEmbedding(text: string, model?: string, cache?: LRUCache<string, Float32Array>): Promise<Float32Array>;
/**
 * Creates a new LRU cache for embeddings with RAM limit
 */
export declare function createEmbeddingCache(ramLimitMB?: number, ttlSeconds?: number): LRUCache<string, Float32Array>;
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