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.

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

Production-ready Snowflake RAG system with type-specific chunking

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 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.

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.

FastAPI service for document chunking and sentence-transformer embeddings for RAG, semantic search, and vector database ingestion.

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

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

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.

KChunker is a lightweight, ultra-fast document parsing and chunking engine designed for RAG systems. It intelligently structures native/scanned PDFs, Excel files, Word documents, and email trails by preserving layout hierarchy, extracting tables, and generating dense vector embeddings for local search databases (ChromaDB and FAISS)