refactx 0.1.2

ReFactX is a scalable method that enables LLMs to access external knowledge without depending on retrievers or auxiliary models. It uses constrained generation with a pre-built prefix-tree index. During inference, to acquire external knowledge, the LLM generates facts with constrained generation which allows only sequences of tokens that form an existing fact.

Install and Try

pip install refactx[torch]

Quick example (print package version)

import refactx
print(refactx.__version__)

Cite

@InProceedings{10.1007/978-3-032-09527-5_16,
      author="Pozzi, Riccardo
      and Palmonari, Matteo
      and Coletta, Andrea
      and Bellomarini, Luigi
      and Lehmann, Jens
      and Vahdati, Sahar",
      editor="Garijo, Daniel
      and Kirrane, Sabrina
      and Salatino, Angelo
      and Shimizu, Cogan
      and Acosta, Maribel
      and Nuzzolese, Andrea Giovanni
      and Ferrada, Sebasti{\'a}n
      and Soulard, Thibaut
      and Kozaki, Kouji
      and Takeda, Hideaki
      and Gentile, Anna Lisa",
      title="ReFactX: Scalable Reasoning with Reliable Facts via Constrained Generation",
      booktitle="The Semantic Web -- ISWC 2025",
      year="2026",
      publisher="Springer Nature Switzerland",
      address="Cham",
      pages="290--308",
      isbn="978-3-032-09527-5",
      doi="10.1007/978-3-032-09527-5_16",
      url="https://doi.org/10.1007/978-3-032-09527-5_16"
}