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

🍱 semantic-chunking ⇢ semantically create chunks from large document for passing to LLM workflows

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

Chunk smarter, not harder — built for LLMs, RAG pipelines, and beyond.

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

A collection of custom n8n nodes for enhanced document processing, text splitting, and embeddings generation

An exploration of text splitting and chunking in JavaScript

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 .

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

I built a News Research Tool with Streamlit and LangChain that fetches news articles from URLs, processes them with text splitting and embeddings, and stores them in a FAISS vector DB. Users can query articles via a RetrievalQA chain to get precise, source-backed insights—showcasing my skills in LLMs and vector search.

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

Text splitting example using Tiktoken

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

A bot that will leverage LangChain and a large language model (LLM) to answer questions based on content from loaded PDF

This is an experiment in learning langchain, pinecone and stuff, don't mind

Matching strings between lists based on length​

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.