A minimal Agentic RAG built with LangGraph — learn Retrieval-Augmented 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.

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.

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

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.

🩺 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.

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

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.

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

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

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

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

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.

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

Research FlowStream — multi‑agent research assistant with Streamlit frontend and FastAPI backend, leveraging LLMs and Qdrant for retrieval, deployed on Render (UI) and Hugging Face Spaces (API)

Documentation assistant for developers who want to quickly understand and query large documentation sites. Built with a modern tech stack including Firecrawl for llm-ready web crawling, Unstructured for document processing, MongoDB Atlas for vector search, and OpenAI for embeddings and generation.

It is the rag based ai model which leverages the misteral-7b model to generate the roadmap for the student information provided.