Hyper-Extract

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"

⚡ 30-Second Quick Start

1. Install:

# Install uv first (if you haven't)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install Hyper-Extract CLI
uv tool install hyperextract
# or: pipx install hyperextract

2. Configure your provider (pick one):

# OpenAI (LLM + embeddings in one key)
he config init -p openai -k YOUR_OPENAI_API_KEY

# DeepSeek (LLM only — pair with an OpenAI embedder, most cost-effective)
he config llm -p deepseek -k YOUR_DEEPSEEK_API_KEY
he config embedder -p openai -k YOUR_OPENAI_API_KEY

# Local vLLM (free, on-premise)
he config llm -p vllm -u http://localhost:8000/v1 -k dummy -m Qwen/Qwen3.5-9B
he config embedder -p vllm -u http://localhost:8001/v1 -k dummy -m BAAI/bge-m3

# Anthropic (Claude) — LLM only
he config llm -p anthropic -k YOUR_ANTHROPIC_API_KEY
he config embedder -p openai -k YOUR_OPENAI_API_KEY

# Google Gemini — LLM only (default: gemini-3.8-flash)
he config llm -p google -k YOUR_GOOGLE_API_KEY
he config embedder -p openai -k YOUR_OPENAI_API_KEY

# Alibaba Bailian (Qwen, LLM + embeddings in one key)
he config init -p bailian -k YOUR_BAILIAN_API_KEY

OpenAI and Bailian provide both LLM and embedding models in one API. Anthropic, Google Gemini, and DeepSeek are LLM-only (pair them with an OpenAI-compatible embedder). DeepSeek is the most cost-effective option (~$0.001-0.005/page).

3. Extract, query & visualize:

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

# Query it
he search ./output/ "What are Tesla's major achievements?"

# Visualize
he show ./output/

# Export to an Obsidian vault (Markdown notes + [[wikilinks]])
he export obsidian ./output/ -o ./vault/

# Your sources change? Feed updates under the same source — old facts roll back automatically
he feed ./output/ updated-tesla.md --source tesla.md

# Tag and scope your searches
he tag ./output/ --source tesla.md --add biography
he search ./output/ "inventions" --tag biography

# Audit: which documents contributed what?
he info ./output/ --sources

Which provider should I use? OpenAI and Bailian provide both LLM and embedding models in one API; Anthropic and DeepSeek are LLM-only (pair with an OpenAI embedder); local vLLM is free but needs a GPU. Full guide: Provider System.

uv pip install hyperextract

from hyperextract import Template

ka = Template.create("general/biography_graph")

with open("examples/en/tesla.md") as f:
    result = ka.parse(f.read())

result.show()

🔗 More examples: examples/en

✨ Core Features

📄 Rich Document Ingestion	Feed PDF, Word, PowerPoint, Excel, HTML, EPUB and more — not just .txt/.md (pip install "hyperextract[ingest]")
🔷 9 Knowledge Structures	From raw chunk corpora and simple Lists to advanced Graphs, Hypergraphs, and Spatio-Temporal Graphs
🧠 11+ Extraction Engines	chunk_rag (zero-cost baseline), GraphRAG, LightRAG, Hyper-RAG, KG-Gen, and more — ready to use
📝 80+ YAML Templates	Zero-code extraction across Finance, Legal, Medical, TCM, Industry, and General domains
🔄 Incremental Evolution & Provenance	Feed new documents anytime — every source is attributed and the index updates incrementally; audit (he info --sources), roll back (he remove --document), or upsert updated versions as your sources change
📤 Obsidian Export	Turn any extracted graph into an Obsidian vault — Markdown notes linked by [[wikilinks]]

🎯 What Can You Do With It?

Feed a 20-page academic paper, get an interactive graph of key concepts, authors, and citations.

he parse paper.pdf -t general/academic_graph -o ./paper_kb/
he show ./paper_kb/

Automatically identify companies, executives, financial metrics, and their relationships from unstructured reports.

he parse earnings.md -t finance/earnings_graph -o ./finance_kb/
he search ./finance_kb/ "What are the key risk factors?"

Run Qwen3.5-9B + bge-m3 locally via vLLM. No data leaves your machine.

from hyperextract import create_client
llm, emb = create_client(
    llm="vllm:Qwen3.5-9B@http://localhost:8000/v1",
    embedder="vllm:bge-m3@http://localhost:8001/v1",
    api_key="dummy",
)

Documents change. Hyper-Extract tracks every source so you can update, roll back, or audit without starting over.

# Ingest with attribution — every fact is traceable to its source
he feed ./ka/ contract-v1.md --source contract-v1
he tag ./ka/ --source contract-v1 --add legal --add acme

# Document updated? Re-feed under the same source — old facts roll back automatically
he feed ./ka/ contract-v2.md --source contract-v1

# Document is obsolete? Roll back everything it contributed
he remove ./ka/ --document contract-v1

# Remove a single wrong fact (LLM-assisted, with dry-run preview)
he remove ./ka/ --edit-node Apple --fact "founded by Steve Jobs" --dry-run

# Search only within legal-tagged documents
he search ./ka/ "termination clause" --tag legal

# Audit: which documents contributed what?
he info ./ka/ --sources

🚀 Supported Platforms & Models

Hyper-Extract uses LangChain structured output with function calling. The model must support tool/function calling.

Platform	Verified Models
OpenAI	gpt-4o, gpt-4o-mini, gpt-5
Anthropic	claude-opus-4-8, claude-sonnet-4-6, claude-haiku-4-5
Google Gemini	gemini-3.8-flash
DeepSeek	deepseek-v4-flash, deepseek-v4-pro
阿里云百炼	qwen-plus, qwen-turbo, deepseek-r1
Local vLLM	Qwen3.5-9B (GPTQ-Marlin)

Embedding models (semantic search) work with any OpenAI-compatible endpoint: text-embedding-3-small, text-embedding-v4 (Bailian), bge-m3 (local vLLM).

DeepSeek: V4 models default to "thinking" mode, which Hyper-Extract auto-disables so structured extraction works. Set DEEPSEEK_API_KEY. DeepSeek has no embeddings API:

from hyperextract import create_client
llm, emb = create_client(llm="deepseek", embedder="openai:text-embedding-3-small")

Anthropic: Claude is used for the LLM (set ANTHROPIC_API_KEY, extra: pip install 'hyperextract[anthropic]'). No embeddings API:

from hyperextract import create_client
llm, emb = create_client(llm="anthropic", embedder="openai:text-embedding-3-small")

Google Gemini: Gemini is used for the LLM (set GOOGLE_API_KEY or GEMINI_API_KEY, extra: pip install 'hyperextract[google]'). Default model is gemini-3.8-flash. No mature embedder path in this repo:

from hyperextract import create_client
llm, emb = create_client(llm="google", embedder="openai:text-embedding-3-small")

📖 Full guide: Provider System & Local Model Support

📈 Why Hyper-Extract?

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

🧩 Supported Knowledge Structures

From simple to complex — pick the right structure for your data:

Example — AutoGraph visualization:

Hyper-Extract follows a three-layer architecture:

• Auto-Types — 9 strongly-typed data structures (Model, List, Set, Graph, Hypergraph, Temporal Graph, Spatial Graph, Spatio-Temporal Graph, Document corpus)
• Methods — Extraction & retrieval algorithms: Chunk-RAG baseline, KG-Gen, GraphRAG, LightRAG, Hyper-RAG, Cog-RAG, and more
• Templates — 80+ presets across 6 domains. Zero-code setup.

Template example (Graph type):

language: en
name: Knowledge Graph
type: graph
tags: [general]
description: 'Extract entities and their relationships.'
output:
  entities:
    fields:
    - name: name
      type: str
    - name: type
      type: str
    - name: description
      type: str
  relations:
    fields:
    - name: source
      type: str
    - name: target
      type: str
    - name: type
      type: str
identifiers:
  entity_id: name
  relation_id: '{source}|{type}|{target}'

• Browse all 80+ templates
• Create custom templates

📰 What's New

v0.12.0 — 📁 Nested directory support: he parse/he feed walk subdirectories recursively; same-named files stay distinct via relative-path source ids.

📰 Full release notes · All releases

📚 Documentation & Resources

Resource	Link
Full Documentation	yifanfeng97.github.io/Hyper-Extract
CLI Guide	Command-line interface
Provider System	Model compatibility & local deployment
News	Release notes & highlights
Template Gallery	80+ presets
Examples	Working code

🔌 MCP Server

Expose your knowledge abstracts to MCP-capable assistants (Claude Desktop, IDE agents) via the Model Context Protocol — read + export only.

pip install 'hyperextract[mcp]'
he-mcp        # stdio MCP server

Tools: list_templates, info, search, ask (RAG), export_obsidian, export_graphml, export_csv, export_jsonld, export_cypher. Full guide: MCP Server docs.

🤝 Contributing & License

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

🔒 Security

This project has been security assessed by MseeP.ai.

AtomGit Mirror

AtomGit mirror - a synchronized AtomGit mirror of Agent Reach for easier access and cloning in China. Hosted on AtomGit: https://atomgit.com/yifanfeng97/Hyper-Extract