Abstract
Language model applications are becoming increasingly popular and complex, often including features like tool usage and retrieval augmentation. However, existing frameworks for such applications are often opinionated, deciding for developers how their prompts ought to be formatted and imposing limitations on customizability and reproducibility. To solve this we present Kani: a lightweight, flexible, and model-agnostic open-source framework for building language model applications. Kani helps developers implement a variety of complex features by supporting the core building blocks of chat interaction: model interfacing, chat management, and robust function calling. All Kani core functions are easily overridable and well documented to empower developers to customize functionality for their own needs. Kani thus serves as a useful tool for researchers, hobbyists, and industry professionals alike to accelerate their development while retaining interoperability and fine-grained control.- Anthology ID:
- 2023.nlposs-1.8
- Volume:
- Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Liling Tan, Dmitrijs Milajevs, Geeticka Chauhan, Jeremy Gwinnup, Elijah Rippeth
- Venues:
- NLPOSS | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 65–77
- Language:
- URL:
- https://aclanthology.org/2023.nlposs-1.8
- DOI:
- 10.18653/v1/2023.nlposs-1.8
- Cite (ACL):
- Andrew Zhu, Liam Dugan, Alyssa Hwang, and Chris Callison-Burch. 2023. Kani: A Lightweight and Highly Hackable Framework for Building Language Model Applications. In Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 65–77, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Kani: A Lightweight and Highly Hackable Framework for Building Language Model Applications (Zhu et al., NLPOSS-WS 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.nlposs-1.8.pdf