Demystify RAG by building it from scratch. Local LLMs, no black boxes - real understanding of embeddings, vector search, retrieval, and context-augmented generation.

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

AI-powered document analysis platform built with Next.js, LangChain, PostgreSQL + pgvector. Upload, organize, and chat with documents. Includes predictive missing-document detection, role-based workflows, and page-level insight extraction.

Open-source, self-hosted alternative to NotebookLM. Chat with your documents, generate audio summaries, and ground AI in your own sources—built with Supabase and N8N on a React frontend.

One-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials, ecosystem guides, and evaluation tools.

Agentic RAG for any scenario. Customize sources, depth, and width

Prototype SDK for RAG development.

HiveMind Protocol - A Local-First, Privacy-Preserving Architecture for Agentic RAG

pdfLLM is a completely open source, proof of concept RAG app.

Open-source, fully private and local alternative to NotebookLM. Chat with your documents, generate audio summaries, and ground AI in your own sources—built with Supabase, N8N on a React frontend using Ollama for local inference

AnythingLLM Embed widget submodule for the main AnythingLLM application

A RAG agent using Google's ADK & Vertex AI that lets set up semantic search across documents in under 2 minutes. Features GCS integration and natural language querying

Open-source toolkit to extract structured knowledge graphs from documents and tables — power analytics, digital twins, and AI-driven assistants.

A Terminal User Interface for AI collaboration on code, using a Retrieval-Augmented Generation (RAG) pipeline designed specifically for Rust code generation and refactoring.

Supacrawler's ultralight engine for scraping and crawling the web. Written in go for maximum performance and concurrency.

Template for AI chatbots & document management using Retrieval-Augmented Generation with vector search and FastAPI.

MediNotes: SOAP Note Generation through Ambient Listening, Large Language Model Fine-Tuning, and RAG

A complete Retrieval-Augmented Generation (RAG) application that demonstrates modern AI capabilities for answering questions about Ultimate Frisbee rules and strategies. This project showcases how to build a production-ready RAG system using cutting-edge technologies.

Build and deploy a full-stack RAG app on AWS with Terraform, using free tier Gemini Pro, real-time web search using Remote MCP server and Streamlit UI with token based authentication.