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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

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

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.

  • Updated Jan 22, 2025
  • JavaScript

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