Hyper-Extract is an LLM framework that transforms unstructured text into strongly-typed knowledge structures, from simple lists to complex knowledge/hyper/spatio-temporal graphs. It follows a three-layer architecture of Auto-Types (8 structural output types), Methods (extraction algorithms), and Templates (domain-specific configurations), and is available as both a CLI tool and a Python SDK.
- Supports 10+ extraction engines including GraphRAG, LightRAG, Hyper-RAG, KG-Gen, and iText2KG
- Offers 80+ ready-to-use domain templates covering Finance, Legal, Medical, TCM, and Industry
- Enables incremental evolution'' feed new documents to expand a knowledge abstract without reprocessing everything
- Unique among compared tools (GraphRAG, LightRAG, KG-Gen) in supporting hypergraphs, spatial graphs, and domain templates simultaneously
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- Supports 10+ extraction engines including GraphRAG, LightRAG, Hyper-RAG, KG-Gen, and iText2KG
- Offers 80+ ready-to-use domain templates covering Finance, Legal, Medical, TCM, and Industry
- Enables incremental evolution'' feed new documents to expand a knowledge abstract without reprocessing everything
- Unique among compared tools (GraphRAG, LightRAG, KG-Gen) in supporting hypergraphs, spatial graphs, and domain templates simultaneously
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This article provides five practical Python scripts designed to automate repetitive and tedious PDF management tasks, facilitating efficient batch processing for various document workflows.
The featured capabilities include:
* pypdf: Merging and splitting PDFs
* pdfplumber: Extracting text and tables
* reportlab: Applying stamps and watermarks
* pymupdf: Redacting sensitive content
* pypdf/pdfplumber: Generating metadata inventories
The featured capabilities include:
* pypdf: Merging and splitting PDFs
* pdfplumber: Extracting text and tables
* reportlab: Applying stamps and watermarks
* pymupdf: Redacting sensitive content
* pypdf/pdfplumber: Generating metadata inventories
Leigh Klotz's research focuses on bridging the gap between paper and electronic worlds, specifically through the development of paper user interfaces. Their work explores how to leverage the inherent advantages of paper – ease of use, portability, and low cost – while integrating it with the capabilities of digital technology. Several papers detail systems for encoding electronic files onto paper, allowing for interaction and data storage within a physical medium. This research also encompasses user interface design for document processing and methods for enhancing reprographic operations.
LlamaAgents Builder allows users to build document agents using natural language, generating agent workflows for tasks like classifying financial statements, extracting data from resumes, and creating multi-document summarization pipelines. It offers a balance between low-code ease of use and the flexibility of custom development, generating Workflows that can be deployed on LlamaCloud or self-hosted.
This repository contains the source code for the summarize-and-chat project. This project provides a unified document summarization and chat framework with LLMs, aiming to address the challenges of building a scalable solution for document summarization while facilitating natural language interactions through chat interfaces.
MarkItDown is an open-source Python utility that simplifies converting diverse file formats into Markdown, designed to prepare data for LLMs and RAG systems. It handles various file types, preserves document structure, and integrates with LLMs for tasks like image description.
Docling is a powerful open-source library for document processing, supporting diverse formats and advanced PDF understanding, with seamless integrations with the gen AI ecosystem.
