Prototype SDK for RAG development.

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.

AnythingLLM Embed widget submodule for the main AnythingLLM application

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

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

x0-GPT is an advanced AI-powered tool that enables you to interact seamlessly with any website or document (including PDFs) using natural language. Whether you're looking to extract specific data, automate tasks, or gain insights, x0-GPT makes it possible with ease. Best of all, it's free and accessible to everyone.

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.

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

Welcome to some case study of data science projects - (Personal Projects).

AI-powered chatbot that provides guidance using YouTube video content as a knowledge base. Built with RAG architecture using LangChain, Qdrant, and Google Gemini AI, ElevenLabs and Streamlit.

An Interactive Infinite Story Generation Framework Based on Multi-Agent

A Retrieval-Augmented Generation (RAG) Chat Bot that provides accurate responses based on available documents. This application uses PostgreSQL with pgvector extension for storing and searching vector embeddings, and OpenAI for generating embeddings and responses.

An open-source template for building a RAG chatbot for your company 🤖

Fullstack RAG as a service template

IAttorney is an intelligent legal assistant built using Flask, LangChain, FAISS, OpenAI, and a RAG (Retrieval-Augmented Generation) pipeline.

A Retrieval-Augmented Generation (RAG) system that leverages Google's Agent Development Kit (ADK) and Qdrant vector database via MCP server.

Retrieval-Augmented Generation (RAG) Explained, covering its working principles, components, benefits, applications, challenges, and future prospects.

End-to-end deployment of a scalable RAG chatbot utilizing LangChain for retrieval-based QnA. The project leverages robust CI/CD practices integrating MLFlow with emphasizes on cost analysis.

The Canada Labour Research Assistant (CLaRA) is a privacy-first LLM-powered RAG AI assistant proposing Easily Verifiable Direct Quotations (EVDQ) to mitigate hallucinations in answering questions about Canadian labour laws, standards, and regulations. It works entirely offline and locally, guaranteeing the confidentiality of your conversations.

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.