From Commands to Prompts: LLM-based Semantic File System for AIOS
Authors: name: Zeru Shi; name: Kai Mei; name: Mingyu Jin; name: Yongye Su; name: Chaoji Zuo; name: Wenyue Hua; name: Wujiang Xu; name: Yujie Ren; name: Zirui Liu; name: Mengnan Du; name: Dong Deng; name: Yongfeng Zhang
Published: 2024-09-23 08:39:16

Abstract: Large language models (LLMs) have demonstrated significant potential in the
development of intelligent applications and systems such as LLM-based agents
and agent operating systems (AIOS). However, when these applications and
systems interact with the underlying file system, the file system still remains
the traditional paradigm: reliant on manual navigation through precise
commands. This paradigm poses a bottleneck to the usability of these systems as
users are required to navigate complex folder hierarchies and remember cryptic
file names. To address this limitation, we propose an LLM-based semantic file
system ( LSFS ) for prompt-driven file management. Unlike conventional
approaches, LSFS incorporates LLMs to enable users or agents to interact with
files through natural language prompts, facilitating semantic file management.
At the macro-level, we develop a comprehensive API set to achieve semantic file
management functionalities, such as semantic file retrieval, file update
monitoring and summarization, and semantic file rollback). At the micro-level,
we store files by constructing semantic indexes for them, design and implement
syscalls of different semantic operations (e.g., CRUD, group by, join) powered
by vector database. Our experiments show that LSFS offers significant
improvements over traditional file systems in terms of user convenience, the
diversity of supported functions, and the accuracy and efficiency of file
operations. Additionally, with the integration of LLM, our system enables more
intelligent file management tasks, such as content summarization and version
comparison, further enhancing its capabilities.