A private, offline, multi-RAGpack LLM RAG app for macOS/iOS. Instant, context-aware answers—your device, your data, your rules.

A Python-based tool for context-based search across text documents using OpenAI embeddings and Chroma vector storage. This system enables efficient querying of document collections by generating vector embeddings, storing them persistently, and retrieving relevant results based on textual queries.

VLib is a digital library platform targeting college library systems that utilises a vector database for discovering required resources and thereby making information accessible to all users irrespective of their knowledge level. It overcomes the incapability of present systems to handle descriptive queries thereby limiting information access.

Medical Chatbot RAG est un assistant conversationnel intelligent conçu pour répondre à des questions médicales courantes, tout en s’appuyant sur des sources fiables et validées. Il utilise un pipeline RAG (Retrieval-Augmented Generation) pour enrichir les réponses d’un LLM avec des documents métiers (mental health)

A modern full-stack e-commerce app with real-time product listing, smart contextual search, and responsive UI — built using React, Tailwind CSS, shadcn/ui, Express.js, Prisma, and PostgreSQL.

Simple Search Engine Using Web Search API & Reactstrap UI for React JS

Mini Python project using ChromaDB + Ollama to answer questions about a JSON product catalog. Demo of vector search + LLM in a dockerized FastAPI app. Logs visible with docker-compose up.

Interactive YouTube Q&A app using Retrieval-Augmented Generation (RAG) and Google Gemini LLM. Ask questions about video transcripts and get accurate answers powered by AI in a simple Streamlit interface.

Intelligent VS Code extension for contextual search with ripgrep, Solr indexing, and AI-powered summaries