yifanfeng97/Hyper-Extract

This page introduces an external open-source repository. It is not an HDATF product.

What it does

Hyper-Extract is a Python library that extracts facts and relations from documents into structured knowledge such as knowledge graphs, and it ships the he CLI and the he-mcp server. It is meant for developers and analysts who want to turn scattered documents into knowledge that supports search and question answering. It reads txt and md files directly, and an optional install extra based on markitdown adds PDF, DOCX, PPTX and XLSX. Results are stored in structures such as lists, sets, graphs, hypergraphs, temporal graphs, spatial graphs, spatio-temporal graphs and document collections, and they can be exported to Obsidian, GraphML, CSV, JSON-LD and Cypher. Users choose among domain YAML templates such as finance, legal, medicine and industry and extraction methods such as GraphRAG, LightRAG, Hyper-RAG and KG-Gen. Extraction needs an LLM that supports function calling, a way of receiving answers in a fixed format, together with an OpenAI-compatible embedding model. According to the provider documentation, Anthropic, Gemini and DeepSeek need a separately paired embedding model, and Bailian qwen-max and deepseek-v3 support only the json_object format and cannot be used. Each document's source and content hash are recorded so only changed documents are processed again, and the content that came from one document can be rolled back on its own. The he-mcp server only reads and exports, offering tools for listing templates, viewing information, search, question answering and export. Knowledge templates accept only Chinese or English as the language option, directory processing reads only the files directly inside the given folder, and PDFs without a text layer need OCR first.

License

Custom license Custom or non-standard license. Read the license file before any reuse.

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