A fast, lightweight and easy-to-use Python library for splitting text into semantically meaningful chunks.

Adaptive Chunking: automatically select the best chunking method per document for RAG. Accepted at LREC 2026.

🍱 Semantically create chunks from large document for passing to LLM workflows

A Python CLI to test, benchmark, and find the best RAG chunking strategy for your Markdown documents.

Kerning-aware text splitting

One library to split them all: Sentence, Code, Docs. Chunk smarter, not harder — built for LLMs, RAG pipelines, and beyond.

A sentence splitting (sentence boundary disambiguation) library for Go. It is rule-based and works out-of-the-box.

JChunk is a lightweight and flexible library designed to provide multiple strategies for text chunking within Java applications

An exploration of text splitting and chunking in JavaScript

Split and analyze text files

a powerful Markdown chunking tool that understands document structure. Unlike naive token splitters, it protects atomic elements (code, math, tables), merges by semantic affinity, and scores chunk quality — ready for RAG and fine-tuning workflows.

A practical guide to 6 document chunking strategies for RAG and LLM applications — Document, Fixed-Size, Recursive, Sentence, Semantic, and Agentic chunking with working code and plain-English explanations.

A web app that allows users to upload PDFs and interact with them through a Q&A interface. The application extracts text from PDFs, generates embeddings, stores them in a FAISS database, and retrieves relevant information to provide context-aware answers using a large language model .

Markdown chunker for RAG. Structure-aware splitting preserves full semantic context; tables split at row boundaries.

A smart C# text splitting library that intelligently chunks text while preserving semantic boundaries. Uses a hierarchical approach with configurable overlap and detailed metadata.

An intelligent chatbot that allows users to upload text-based Ayurveda PDFs and ask questions based on the content using RAG (Retrieval-Augmented Generation) combining semantic search and LLM-based responses.

Benchmark chunking strategies for your RAG corpus. Get a recommended config. CLI, Python library, and MCP server.

General token-aware chunker for text + code (AST-aware, never splits a function) + markdown — the reusable chunking primitive for RAG. Stdlib only.

Specialized markdown text splitter - part of LEDAA project's data ingestion pipeline for RAG.

LangChain is a framework, which is very helpful and easy to build applications based on available Large Language Models.