LangChain text splitter

LangChain text splitter is a preprocessing component that divides large documents into smaller, manageable chunks for efficient processing by Large Language Models (LLMs). It addresses the token limit constraints of LLMs by intelligently segmenting text while preserving semantic coherence and context boundaries. The framework offers multiple splitting strategies including RecursiveCharacterTextSplitter, which breaks text hierarchically by paragraphs, sentences, and characters, and specialized splitters for code, HTML, and markdown formats. Advanced features include chunk overlap configuration to maintain context continuity, metadata preservation during splitting, and custom separator definitions for domain-specific content. Text splitters integrate seamlessly with vector databases and embedding models in Retrieval-Augmented Generation (RAG) pipelines, ensuring optimal chunk sizes for both storage efficiency and retrieval accuracy. The component supports configurable parameters such as chunk size, overlap length, and length measurement functions, enabling fine-tuned control over document segmentation based on specific use case requirements and model capabilities.