pdf-processing

Document chatbot — multiple files, topics, chat windows and chat history. Powered by GPT.

library supporting NLP and CV research on scientific papers

Multiple and Large PDF Documents Text Extraction.

A boilerplate solution for processing image and PDF documents for regulated industries, with lineage and pipeline operations metadata services.

Python scripts that converts PDF files to text, splits them into chunks, and stores their vector representations using GPT4All embeddings in a Chroma DB. It also provides a script to query the Chroma DB for similarity search based on user input.

A NPM Package built on top of pdf-lib that provides functonalities like merge, rotate, split,download pdf to disk and many more...

LangGraphRAG: A terminal-based Retrieval-Augmented Generation system using LangGraph. Features include message history caching, query transformation, and vector database retrieval. Ideal for NLP researchers and developers working on advanced conversational AI and information retrieval systems.

Built with pdf-actions NPM package.

An all-in-one GUI management toolkit built with PyQt6, offering a suite of tools for file synchronization, media organization, PDF merging, code formatting, and more.

AI-powered RAG-based tool for summarizing, extracting insights, and answering questions about research papers with high accuracy

📚 AI-Powered Book PDF Knowledge Extractor & Summarizer Transform your PDF books into structured knowledge effortlessly! This tool leverages AI to analyze books page by page, extracting key insights, definitions, and concepts, and organizes them into Markdown summaries for easier study

A side project to easily get and annotate questions and answers to the PsychometryBot project DB using computer vision and pdf parsing

The Document Summarizer leverages Hugging Face’s facebook/bart-large-cnn model to transform lengthy documents into concise summaries. Built with ReactJS (Vite) for the frontend and Flask for the backend, it supports PDF and text files, offering real-time summarization for researchers, students, and professionals.

This is some useful mini projects that I had worked for self-learning Python programming.

PdfSnipper is a lightweight and efficient Python package designed to simplify the management of PDF files, pages, and their conversions during various NLP, Computer Vision (CV), or other data processing tasks. The package eliminates the need for repetitive code by providing intuitive, ready-to-use functions for common PDF-related operations.

Azure Document Intelligence Result Processor: A toolset for annotating PDFs based on Azure Document Intelligence analysis results, featuring a React web application and a standalone Python script for processing and visualizing extracted data with confidence indicators.

Opinionated and Sophisticated Document Region Analyzer.

A web application for preparing books and magazines for offset printing. Automatically arranges PDF pages for commercial A3 printing, supporting both Arabic (RTL) and English (LTR) books. تطبيق ويب لتحضير الكتب والمجلات للطباعة على مطابع الأوفست. يقوم تلقائياً بترتيب صفحات PDF للطباعة التجارية على ورق A3، مع دعم الكتب العربية والإنجليزية.

This project implements a Retrieval Augmented Generation (RAG) system that answers questions based on the PDF document. It utilizes Weaviate as a vector database for efficient retrieval of relevant information and Gemini to generate natural language responses.

A powerful Retrieval Augmented Generation (RAG) application built with NVIDIA AI endpoints and Streamlit. This solution enables intelligent document analysis and question-answering using state-of-the-art language models, featuring multi-PDF processing, FAISS vector store integration, and advanced prompt engineering.