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

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.

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

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.

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

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

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.

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

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

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.