Smart Knowledge Extraction CLI

Transform documents into structured knowledge with one command.

📖 English Version · 中文版

"Stop reading. Start understanding."
"告别文档焦虑，让信息一目了然"

Hyper-Extract is an intelligent, LLM-powered knowledge extraction and evolution framework. It radically simplifies transforming highly unstructured texts into persistent, predictable, and strongly-typed Knowledge Abstracts. It effortlessly extracts information into a wide spectrum of formats—ranging from simple Collections (Lists/Sets) and Pydantic Models, to complex Knowledge Graphs, Hypergraphs, and even Spatio-Temporal Graphs.

✨ Core Features

• 🔷 8 Auto-Types: From basic AutoModel/AutoList to advanced AutoGraph, AutoHypergraph, and AutoSpatioTemporalGraph.
• 🧠 10+ Extraction Engines: Out-of-the-box support for cutting-edge retrieval paradigms like GraphRAG, LightRAG, Hyper-RAG, and KG-Gen.
• 📝 Declarative YAML Templates: Zero-code extraction definition. Includes 80+ presets across 6 domains.
• 🔄 Incremental Evolution: Feed new documents on the fly to continuously map out and expand the extracted knowledge.

⚡ Quick Start

1. Installation

For CLI Users (install he command globally):

uv tool install hyperextract

For Python Developers (use as library):

uv pip install hyperextract

2. The Command Line Way

Extract, search, and manage directly from CLI.

By default, the CLI uses gpt-4o-mini and text-embedding-3-small.

# Configure OpenAI API Key
he config init -k YOUR_OPENAI_API_KEY
Extract knowledge
he parse examples/en/tesla.md -t general/biography_graph -o ./output/ -l en
Query the knowledge abstract
he search ./output/ "What are Tesla's major achievements?"
Visualize the knowledge graph
he show ./output/
Incrementally supplement knowledge
he feed ./output/ examples/en/tesla_question.md
Show the updated knowledge graph
he show ./output/

# Clone the repository
git clone https://github.com/yifanfeng97/hyper-extract.git
cd hyper-extract
Install dependencies
uv sync

# Copy the example env file
cp .env.example .env
Edit .env with your API key and base URL
OPENAI_API_KEY=your-api-key
OPENAI_BASE_URL=https://api.openai.com/v1

import os
from dotenv import load_dotenv
Load environment variables from .env file
load_dotenv()
from hyperextract import Template
Create a template
ka = Template.create("general/biography_graph")
Parse a document
with open("examples/en/tesla.md", "r", encoding="utf-8") as f:
text = f.read()
result = ka.parse(text)
Visualize the knowledge graph
ka.show(result)
Incrementally supplement knowledge
with open("examples/en/tesla_question.md", "r", encoding="utf-8") as f:
new_text = f.read()
ka.feed(result, new_text)
Show the updated knowledge graph
ka.show(result)

Installation Comparison:

🧩 Deep Dive: The 8 Auto-Types

Our framework embraces complexity without making you write boilerplate code.

Example: AutoGraph Visualization

Here is the knowledge graph visualization after AutoGraph extraction:

🛠️ Architecture Overview

Hyper-Extract follows a three-layer architecture:

• Auto-Types define the data structures for knowledge extraction. With 8 strong-typed structures (AutoModel, AutoList, AutoSet, AutoGraph, AutoHypergraph, AutoTemporalGraph, AutoSpatialGraph, AutoSpatioTemporalGraph), they serve as the output format for all extractions.

• Methods provide extraction algorithms built on Auto-Types. This includes Typical methods (KG-Gen, iText2KG, iText2KG*) and RAG-based methods (GraphRAG, LightRAG, Hyper-RAG, HypergraphRAG, Cog-RAG).

• Templates offer domain-specific configurations with ready-to-use prompts and data structures. Covering 6 domains (Finance, Legal, Medical, TCM, Industry, General) with 80+ preset templates, users can extract knowledge without dealing with Auto-Types or Methods directly.

Use via CLI (he parse, he search, he show...) or Python API (Template.create()).

📚 Related Documentation

• Preset Templates: Browse 80+ ready-to-use templates across 6 domains
• Design Guide: Learn how to create custom templates

language: en
name: Knowledge Graph
type: graph
tags: [general]
description: 'Extract entities and their relationships to construct a knowledge graph.'
output:
entities:
fields:
- name: name
type: str
description: 'Entity name'
- name: type
type: str
description: 'Entity type: e.g., person, organization, event'
- name: description
type: str
description: 'Entity description'
relations:
fields:
- name: source
type: str
description: 'Source entity'
- name: target
type: str
description: 'Target entity'
- name: type
type: str
description: 'Relation type: e.g., invention, collaboration, competition'
- name: description
type: str
description: 'Relation description'
guideline:
target: 'Extract entities and their relationships from the text.'
rules_for_entities:
- 'Extract meaningful entities'
- 'Maintain consistent naming'
rules_for_relations:
- 'Create relations only when explicitly expressed in the text'
identifiers:
entity_id: name
relation_id: '{source}|{type}|{target}'
relation_members:
source: source
target: target
display:
entity_label: '{name} ({type})'
relation_label: '{type}'

📈 Comparison with Other Libraries

📚 Related Documentation

• Documentation - Complete documentation site
• CLI Guide - Command-line interface
• Template Gallery - Available templates
• Example Code - Working examples

🤝 Contributing & License

Contributions are welcome! Please submit Issues and PRs. Licensed under Apache-2.0.

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