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 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.
