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pdf-processing

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document-processing-pipeline-for-regulated-industries

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

  • Updated Oct 25, 2021
  • Python

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.

  • Updated Jul 13, 2024
  • Python

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.

  • Updated Feb 3, 2025
  • Python

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.

  • Updated Jan 6, 2025
  • Python

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.

  • Updated Oct 31, 2024
  • Python

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.

  • Updated Jan 12, 2025
  • Jupyter Notebook

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