A fast, lightweight and easy-to-use Python library for splitting text into semantically meaningful chunks.
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Updated
Oct 28, 2025 - Python
A fast, lightweight and easy-to-use Python library for splitting text into semantically meaningful chunks.
A package for parsing PDFs and analyzing their content using LLMs.
🍱 semantic-chunking ⇢ semantically create chunks from large document for passing to LLM workflows
🧠✂️ SemanticSlicer — A smart text chunker for LLM-ready documents.
Embedding-driven, context-aware text chunking for Semantic Kernel and RAG workflows in .NET
This project is designed to extract text from documents and prepare it for processing by Large Language Models (LLM). Implemented a feature to store and utilize text style information, enabling the program to identify and segment content based on potential headers and titles.
Cutting-edge tool designed to intelligently segment text documents into optimally-sized chunks
A lightweight TypeScript text splitter for RAG applications
An exploration of text splitting and chunking in JavaScript
Preprocess document service for RAG (Retriveal Augumented Generation)
This Text Summarization Tool uses advanced machine learning models to create concise, meaningful summaries of lengthy texts. Built with Hugging Face Transformers and Gradio, it efficiently handles various input lengths, ideal for summarizing articles, reports, and more
A service-oriented .NET library for AI with interchangeable orchestrations and vector stores.
Smart text chunker for LLM preprocessing (sections → paragraphs → sentences → hard splits).
End-to-end Retrieval Augmented Generation (RAG) pipeline using Notion, Qdrant, Sentence Transformers, and Streamlit for interactive question answering on private Notion workspaces.
A lightweight, modular Retrieval-Augmented Generation (RAG) system built with Streamlit, FAISS, and LLMs like OpenAI and Ollama. Upload documents, embed them, and ask intelligent questions with real-time context-aware responses.
Curso completo de Agentes IA con LangChain, LangRAP y n8n. Incluye ejemplos prácticos, agentes simples, agentes que resumen PDFs, agentes girly, gestión de entornos, variables de entorno y buenas prácticas con GitHub.
Presenting, Cubastion's HR chatbot - it can answer queries based on all the latest HR documents published by Cubastion's HR team. This conveniently saves time, allowing a Cubastion employee to resolve their query without having to comb through the actual documents. <<Developed with Python, sentence-transformers, Pinecone, llama3.2, and Streamlit>>
Presenting, Cubastion's HR chatbot - it can answer queries based on all the latest HR documents published by Cubastion's HR team. This conveniently saves time, allowing a Cubastion employee to resolve their query without having to comb through the actual documents. <<Developed with Python, sentence-transformers, Pinecone, llama3.2, and Streamlit>>
Vector Storage and Retrieval for RAG
This project aims to build an AI-powered Legal Advisor that leverages natural language processing and vector search technology to provide users with legal guidance based on authoritative legal texts.
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