Chat with PDF lets you ask questions to PDF documents. Built and deployed with NuxtHub, and powered by Cloudflare Workers AI and Vectorize.

A Retrieval-Augmented Generation (RAG) application for querying legal documents. It uses PostgreSQL, Elasticsearch, and LLM to provide summaries and suggestions based on user queries. Features data ingestion with Airflow, real-time monitoring with Grafana, and a Streamlit interface.

GenAI/RAG Optimizer and Toolkit for experimentation using Oracle Database AI Vector Search

My attempt at implementing retreival augmented generation on Ollama and other LLM services using chromadb and langchain while also providing an easy to understand, clean code for others since nobody else does

👨🏻‍💻 Meet Lumina – my personal chatbot assistant designed to answer any questions, either about me or any other topics. Powered by Retrieval-Augmented Generation (RAG), LangChain, kNN cosine similarilty search, and Pinecone vector database, Lumina offers intelligent, accurate, and speedy responses tailored for all conversations.

A new novel multi-modality (Vision) RAG architecture

A collection of AI tutorials from Dr. Ashish Bamania

A very CPU-friendly RAG implementation

This project is an innovative coffee shop application designed to bring an engaging and personalized experience to coffee lovers. The app leverages AI-powered agents for chat-based interactions and integrates modern web and mobile development techniques to provide seamless ordering and delivery services.

RAG with web search, self correction, gap detection and query loops

A RAG based LLM assistant for australian first home buyers

This repository contains an end-to-end Retrieval-Augmented Generation (RAG) system that leverages Deepseek and Ollama to provide intelligent responses based on any pdf content.

RAG Chatbot built using Cloudflare's AI model.

This project is a comprehensive RAG pipeline implementation that includes YouTube and web scraping tools for data collection, Milvus as a vector database for efficient context retrieval, and a Tkinter-based multi-user chatbot interface. It also features data visualization tools enhanced with PyCUDA for analyzing large datasets.

A very simple RAG implementation

The goal of this project is to develop a RAG system using Agent from LangGraph to improve the travelling experience of tourists.

CLI-based LanceDB vector embedding with LLM integration for RAG workflows

turn any platform page into markdown!

Agentic Rag with Customizable Sub-Agents.

ChatBot for live scores of cricket matches.