A modular 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.

A hands‑on RAG experimentation lab. Largely configurable with debug insights. Classification‑driven corpus construction, filter chains, document loading, chat interaction, Open WebUI integration. Experimental by design and not production‑ready.

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.

Structural Memory Protocol (SMP) — live structural code graph for scalable, safe AI agents.

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

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.

🩺 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.

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

RAG SaaS - upload PDFs, ask questions, AI answers strictly from your document

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.

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

Enterprise Agentic RAG platform built with FastAPI, LangGraph, OpenAI, PostgreSQL, Qdrant Cloud, Railway, and Streamlit. Features multi-agent orchestration, hybrid retrieval (Vector Search + BM25), CrossEncoder reranking, conversational memory, tool-calling, web-search augmentation, observability, and cloud deployment.

A hands-on repository of practical AI apps and LLM-based solutions, including AI agents and RAG systems.

The RAG Pipeline Utils library offers a production-ready, modular framework for building retrieval-augmented generation (RAG) pipelines — plug in custom loaders, embedders, retrievers and LLMs, and deploy secure, observable, high-performing workflows with ease.

MediQuery.ai — A Production-Grade Medical RAG Pipeline. Built with LangChain, Groq (Llama-3), and Pinecone. Fully containerized with Docker and deployed on AWS (EC2/ECR) using GitHub Actions CI/CD. 🩺🤖

Enterprise-grade local RAG API assistant backend running on traditional Azure CPU infrastructure. Serves a quantized Llama 3.2 GGUF model via llama-server with offline ChromaDB ingestion and stateful MySQL transaction logging.