A minimal Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.

Client-side retrieval firewall for RAG systems — blocks prompt injection and secret leaks, re-ranks stale or untrusted content, and keeps all data inside your environment.

AI-Rag-ChatBot is a complete project example with RAGChat and Next.js 14, using Upstash Vector Database, Upstash Qstash, Upstash Redis, Dynamic Webpage Folder, Middleware, Typescript, Vercel AI SDK for the Client side Hook, Lucide-React for Icon, Shadcn-UI, Next-UI Library Plugin to modify TailwindCSS and deploy on Vercel.

An advanced, fully local, and GPU-accelerated RAG pipeline. Features a sophisticated LLM-based preprocessing engine, state-of-the-art Parent Document Retriever with RAG Fusion, and a modular, Hydra-configurable architecture. Built with LangChain, Ollama, and ChromaDB for 100% private, high-performance document Q&A.

A powerful RAG tool that scrapes YouTube channel videos, extracts transcripts, and enables AI-powered chat interactions using Google's Gemini API.

RAGify is a modern chat application that provides accurate, hallucination-free answers by grounding responses in your documents. No more made-up information - if the answer isn't in your knowledge base, RAGify tells you so.

🩺 RAGnosis — An AI-powered clinical reasoning assistant that retrieves real diagnostic notes (from MIMIC-IV-Ext-DiReCT) and generates explainable medical insights using Mistral-7B & FAISS, wrapped in a clean Gradio UI. ⚡ GPU-ready, explainable, and open-source.

LLMlight is a lightweight Python library for running local language models with built-in memory, retrieval, and prompt optimization, requiring minimal dependencies.

🚀 Build a production-ready Agentic RAG system with LangGraph using minimal code and streamline your AI development process.

A RAG-based retrieval system for air pollution topics using LangChain and ChromaDB.

RAG Mini Project — Retrieval‑Augmented Generation chatbot with FastAPI backend (Docker on Hugging Face Spaces) and Streamlit frontend (Render), featuring document ingestion, vector search, and LLM‑powered answers

KardiaFlow is a specialized medical RAG system for analyzing clinical documents. Leveraging Chroma, Sentence-Transformers, and Ollama (gemma2:2b), it transforms PDFs into insights. With FastAPI, Docker, and Azure CI/CD, it offers a robust, secure architecture for professional healthcare AI deployment.

Domain Chatbot is a web-based AI chatbot designed for small and medium enterprises (SMEs). It helps users get accurate answers by understanding company policies, documents, and other related content. It uses retrieval-augmented generation (RAG) with LLM models to provide precise and relevant responses.

A comprehensive, hands-on tutorial repository for learning and mastering LangChain - the powerful framework for building applications with Large Language Models (LLMs). This codebase provides a structured learning path with practical examples covering everything from basic chat models to advanced AI agents, organized in a progressive curriculum.

Modular RAG framework with semantic chunking, hybrid retrieval, and reranking. Built to be simple (3-line setup) but production-ready.

Production-grade Retrieval-Augmented Generation (RAG) backend in TypeScript with Express.js, PostgreSQL, and Sequelize — featuring OpenAI-powered embeddings, LLM orchestration, and a complete data-to-answer pipeline.

An AI-powered HR assistant that uses Retrieval-Augmented Generation (RAG) with FastAPI & Streamlit to answer employee queries, search profiles, and simplify HR resource management

A doctor-assistive AI system that interprets medical knowledge and patient images simultaneously. It utilizes a Dual-Encoder architecture to cross-reference textbook theory with visual pathology, generating clinically grounded diagnoses.

Repository for the take-home midterm of CENG 543 Information Retrieval Course

RAG-PDF Assistant — A simple Retrieval-Augmented Generation (RAG) chatbot that answers questions using custom PDF documents. It uses HuggingFace embeddings for text representation, stores them in a Chroma vector database, and generates natural language answers with Google Gemini. In this example, the assistant is powered by a few school policy doc