NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

Content-Addressable Data Synchronization Tool

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

An extensible Java framework for building event-driven applications that break up XML and non-XML data into chunks for data integration

Fully neural approach for text chunking

Alternative casync implementation

The RAG Experiment Accelerator is a versatile tool designed to expedite and facilitate the process of conducting experiments and evaluations using Azure Cognitive Search and RAG pattern.

A package for parsing PDFs and analyzing their content using LLMs.

A new chunking strategy developed by ZeroEntropy for general semantic chunking using Llama-70B.

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

A TensorFlow implementation of Neural Sequence Labeling model, which is able to tackle sequence labeling tasks such as POS Tagging, Chunking, NER, Punctuation Restoration and etc.

Pure Rust PDF library for AI/RAG: structure-aware chunking, no ML, no C deps.

PDFStract - Extract, Chunking and Embedding Layer in Your RAG Pipeline - Available as CLI - WEBUI - API

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

Open-source toolkit for reliable RAG pipelines: convert PDFs to Markdown, clean documents, inspect chunks, compare chunking strategies, and enrich metadata for LLM applications.

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

Postgres extensions to support end-to-end Retrieval-Augmented Generation (RAG) pipelines

Live TS segmenter and HLS manifest creation in Go

An LLM GUI application; enables you to interact with your files, offering dynamic parameters that can modify response behavior during runtime.

An Overview of the Latest Document Chunking Research