Document-Analysis-Identification-with-RAG-Vector-Database-and-Mistral-LLM

Open-source platform for extracting structured data from documents using AI.

This repository provides train＆test code, dataset, det.&rec. annotation, evaluation script, annotation tool, and ranking.

Code for the paper "PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks" (ICPR 2020)

Official PyTorch implementation of LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding (ACL 2022)

A package for parsing PDFs and analyzing their content using LLMs.

RObust document image BINarization

Powerful web application that combines Streamlit, LangChain, and Pinecone to simplify document analysis. Powered by OpenAI's GPT-3, RAG enables dynamic, interactive document conversations, making it ideal for efficient document retrieval and summarization.

Improving Document Binarization via Adversarial Noise-Texture Augmentation (ICIP 2019)

Code for ICPR2022 paper: "Graph Neural Networks and Representation Embedding for table extraction in PDF Documents"

Service to import data from various sources and index it in AI Search. Increases data relevance and reduces final size by 90%+. Useful for RAG scenarios with LLM. Hosted in Azure with serverless architecture.