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RAG retrieval augmented generation

This repository provides an offline AI-Agent Terminal that integrates Retrieval-Augmented Generation (RAG) with locally hosted distilled language models, GPU-accelerated embedding and generation, rotating logs, and an interactive console workflow.

KiM Explorer is a two-stage RAG application for transport policy research publications from the KiM Netherlands Institute for Transport Policy Analysis. Users perform semantic search to identify relevant documents, manually select publications, then interact with an LLM using full document context rather than chunks. Built with Python/NiceGUI/OpenAI API, featuring citation generation, conversation history, filtering, and web/CLI interfaces. https://explorer.kim.rijkscloud.nl/

A Rag application that keeps you updated about AI news, scrapped from MIT AI newsletter website

AIQA (AI Questions Answering) — локальный стек для построения сервиса вопросов-ответов на базе LLM и векторных представлений. Быстрое развертывание в Docker, устойчивое хранение знаний и удобный API для интеграции.

RAGtime is a minimal implementation of a Retrieval Augmented Generation (RAG) Pipeline designed to facilitate chatting with your documents

Using RAG on news articles to overcome LLMs knowledge cutoff.

Euclidean Distance or Cosine Similarity using CSV files as vectors embedding DBs - written in Go