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HYPER-EXTRACT
Transform unstructured text into structured knowledge with LLMs
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ABOUT
Unstructured text — PDFs, emails, web pages, chat logs — is hard for LLMs to work with effectively. Without structured context, retrieval is imprecise and reasoning lacks depth. Hyper-Extract uses LLMs to build hypergraphs and knowledge graphs from raw documents, transforming them into queryable, structured knowledge. The CLI makes this process repeatable and scriptable, with semantic search built in.
INSTALL
pip install hyperextractINTEGRATION GUIDE
1. Extract entities, relationships, and facts from legal contracts to build a searchable knowledge base
2. Convert a corpus of technical documentation into hypergraph structures for semantic retrieval
3. Build spatio-temporal knowledge graphs from news articles to track events across time and location
4. Process PDF reports into structured data for downstream RAG pipelines and analytics
5. Encode unstructured customer feedback into knowledge graphs for trend analysis and root cause discovery
TAGS
pythonragknowledge-graphllmcliinformation-extractionhypergraph