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

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

Your RAG pipeline is broken and you don't know it. Chunky lets you validate your Markdown and choose the best chunking strategy before indexing.

Production-ready Snowflake RAG system with type-specific chunking

A lightweight Python library for metadata-rich document chunking in Retrieval-Augmented Generation (RAG) workflows. It leverages Azure AI Document Intelligence to enhance chunking by retaining hierarchical structure, page numbers, and bounding boxes for seamless integration with PDF viewers.

📝 Parse, chunk, and evaluate Markdown for RAG pipelines with token-accurate support and flexible strategies for optimal context management.

"My complete LangChain learning journey — from basics to advanced RAG, LCEL, LangGraph, LangServe, LangSmith with hands-on code examples."

A Controlled Natural Language (CNL) for AI designed to "minify" language and make AI context denser.

Astra Vector DB on Python-paketti, joka tallentaa dokumentteja DataStax Astra DB -vektoritietokantaan ja suorittaa semanttista hakua.

This repository provides a fully modular implementation of a Retrieval-Augmented Generation (RAG) pipeline tailored for Italian legal-domain documents.

building a CPU-Only "PDF Q&A System" using hugging face, chromaDB vector search, and Python

Smart text chunking tool for RAG systems. Splits long texts into sentence-based chunks with ~10%-15% overlap for better context retention. Runs fully in-browser with a clean UI and copyable outputs.