Page setting templetes and document processing scripts for zine making

This RAG stack can run on a raspberry pi (16GB CPU)

RAG-Enabled is a Python-based assistant that combines Retrieval-Augmented Generation (RAG), LangChain, and OpenAI to deliver intelligent responses. It supports multi-format document loading, context retrieval via ChromaDB, and auto-compresses chat history after 8 messages to maintain memory efficiency.

An on-going chatbot project. Deployed on Heroku and Github Page.

OCR Text Extractor – Cloud & Local Vision API Integration This application is designed to extract text from images using Optical Character Recognition (OCR) via multiple providers: Google Vision API Azure Cognitive Services Tesseract OCR (Local Engine)

A Bot that downloads invoices from Customer Invoice Portal. Pulls specific details from invoices. Extract the details into an excel file

A Streamlit-based AI chatbot that allows users to upload PDFs and ask questions about their content using Google's Generative AI.

This project is an implementation of Retrieval-Augmented Generation (RAG) using Streamlit. It integrates various document loaders and NLP models to allow users to upload documents, process them into text chunks, and create vector stores for efficient retrieval.

This sample shows how to merge documents with JSON data using the DocumentProcessing Web API.

Built an advanced RAG system that can intelligently query and synthesize information from diverse academic materials, delivering context-aware responses with remarkable accuracy.

This sample shows how to merge a template created with DocumentEditor using DocumentProcessing