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Transform documents into structured knowledge with one command.

"告别文档焦虑，让信息一目了然"

Hyper-Extract is an intelligent, LLM-powered knowledge extraction framework. It transforms unstructured text into persistent, predictable, and strongly-typed knowledge structures—from simple lists to complex knowledge graphs, hypergraphs, and spatio-temporal graphs.

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⚡ 5-Minute Quick Start

CLI (Terminal)Python

# 1. Install CLI tool
uv tool install hyperextract

# 2. Configure API Key
he config init -k YOUR_OPENAI_API_KEY

# 3. Extract knowledge from a document
he parse tesla.md -t general/biography_graph -o ./output/ -l en

# 4. Visualize the knowledge graph
he show ./output/

from hyperextract import Template

# 1. Create a template
ka = Template.create("general/biography_graph", "en")

# 2. Extract knowledge
with open("tesla.md") as f:
    result = ka.parse(f.read())

# 3. Visualize
ka.show(result)

→ Ready to dive deeper? Check out the Getting Started Guide or jump to CLI / Python SDK documentation.

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✨ What Makes Hyper-Extract Different?

• 8 Auto-Types

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  From simple AutoList/AutoModel to advanced AutoGraph, AutoHypergraph, and AutoSpatioTemporalGraph. Pick the right structure for your data.

• 10+ Extraction Engines

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  Built-in support for GraphRAG, LightRAG, Hyper-RAG, KG-Gen, iText2KG, and more. Choose the best method for your use case.

• 80+ Domain Templates

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  Ready-to-use templates for Finance, Legal, Medical, TCM, and Industry. Zero configuration needed.

• Incremental Evolution

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  Feed new documents to continuously expand your knowledge abstract. No need to reprocess everything.

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🎯 Choose Your Path

• CLI User

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  Process documents directly from your terminal. Perfect for:

  • Quick knowledge extraction
  • Batch document processing
  • Building knowledge abstracts without coding

  CLI Guide

• Python Developer

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  Integrate into your Python applications. Perfect for:

  • Custom extraction pipelines
  • Integration with existing workflows
  • Building AI-powered applications

  Python SDK

• Want to Learn More?

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  Understand the concepts and architecture:

  • How Auto-Types work
  • Choosing extraction methods
  • Creating custom templates

  Concepts

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🧩 The 8 Auto-Types at a Glance

Type	Use Case	Example Output
AutoModel	Structured summaries	A pydantic model with specific fields
AutoList	Collections of items	A list of entities or facts
AutoSet	Deduplicated collections	A set of unique items
AutoGraph	Entity-relationship networks	Knowledge graph with nodes and edges
AutoHypergraph	Multi-entity relationships	Hyperedges connecting multiple nodes
AutoTemporalGraph	Time-based relationships	Graph with temporal information
AutoSpatialGraph	Location-based relationships	Graph with geographic data
AutoSpatioTemporalGraph	Time + Space combined	Full context with when and where

→ Learn which Auto-Type to choose

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🏗️ Architecture Overview

Hyper-Extract follows a three-layer architecture:

graph TD
    A[Your Document] --> B[CLI / Python API]
    B --> C[Template]
    B --> D[Method]
    C --> E[Auto-Type]
    D --> E
    E --> F[Structured Knowledge]

    subgraph "Layer 3: Templates & Methods"
        C
        D
    end

    subgraph "Layer 2: Core Engine"
        E
    end

    subgraph "Layer 1: Output"
        F
    end

1. Auto-Types — Define the output data structure (8 types)
2. Methods — Provide extraction algorithms (RAG-based and Typical)
3. Templates — Offer domain-specific, ready-to-use configurations

You can use Hyper-Extract at any level: pick a template for quick results, choose a method for more control, or work directly with Auto-Types for full customization.

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📊 Comparison with Other Tools

Feature	GraphRAG	LightRAG	KG-Gen	Hyper-Extract
Knowledge Graph	✅	✅	✅	✅
Temporal Graph	✅	❌	❌	✅
Spatial Graph	❌	❌	❌	✅
Hypergraph	❌	❌	❌	✅
Domain Templates	❌	❌	❌	✅
CLI Tool	✅	❌	❌	✅
Multi-language	✅	❌	❌	✅

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📚 Documentation Structure

• Getting Started — Installation and your first extraction
• CLI Guide — Complete terminal workflow documentation
• Python SDK — API reference and developer guides
• Concepts — Understanding the architecture
• Templates — Domain-specific extraction templates
• Resources — FAQ, troubleshooting, and contributing

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🤝 Contributing

Contributions are welcome! Whether it's bug reports, feature requests, or documentation improvements, please feel free to submit an issue or pull request.

GitHub Repository

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📄 License

Hyper-Extract is licensed under the Apache-2.0 License.