@juspay/neurolink
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Universal AI Development Platform with working MCP integration, multi-provider support, voice (TTS/STT/realtime), and professional CLI. 58+ external MCP servers discoverable, multimodal file processing, RAG pipelines. Build, test, and deploy AI applicatio
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TypeScript
/**
* Token-based Chunker
*
* Splits text based on token counts using simple tokenization.
* Best for controlling context window usage with LLMs.
*/
import type { Chunker, Chunk, ChunkerValidationResult, TokenChunkerConfig, BaseChunkerConfig } from "../../types/index.js";
/**
* Token-aware chunker implementation
* Splits text based on approximate token counts
*
* Note: Uses simple word-based tokenization as approximation.
* For exact token counts, integrate with tiktoken or model-specific tokenizers.
*/
export declare class TokenChunker implements Chunker {
readonly strategy: "token";
private readonly CHARS_PER_TOKEN;
chunk(text: string, config?: TokenChunkerConfig): Promise<Chunk[]>;
/**
* Simple word-based tokenization
*/
private tokenize;
/**
* Get characters per token for a tokenizer
*/
private getCharsPerToken;
/**
* Estimate average tokens per word
*/
private estimateTokensPerWord;
/**
* Estimate token count for text
*/
estimateTokenCount(text: string, tokenizer?: string): number;
validateConfig(config: BaseChunkerConfig): ChunkerValidationResult;
}