title: From Commands to Prompts: LLM-based Semantic File System for AIOS
summary: 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.
paper_authors._id: 6711adca41ad206aabacc032
paper_authors.name: Zeru Shi
paper_authors._id: 6711adca41ad206aabacc033
paper_authors.name: Kai Mei
paper_authors._id: 6711adca41ad206aabacc034
paper_authors.name: Mingyu Jin
paper_authors._id: 6711adca41ad206aabacc035
paper_authors.name: Yongye Su
paper_authors._id: 6711adca41ad206aabacc036
paper_authors.name: Chaoji Zuo
paper_authors._id: 6711adca41ad206aabacc037
paper_authors.name: Wenyue Hua
paper_authors._id: 6711adca41ad206aabacc038
paper_authors.name: Wujiang Xu
paper_authors._id: 6711adca41ad206aabacc039
paper_authors.name: Yujie Ren
paper_authors._id: 6711adca41ad206aabacc03a
paper_authors.name: Zirui Liu
paper_authors._id: 6711adca41ad206aabacc03b
paper_authors.name: Mengnan Du
paper_authors._id: 6711adca41ad206aabacc03c
paper_authors.name: Dong Deng
paper_authors._id: 6711adca41ad206aabacc03d
paper_authors.name: Yongfeng Zhang
paper_title: From Commands to Prompts: LLM-based Semantic File System for AIOS
paper_summary: 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.
paper_ai_summary: An LLM-based semantic file system enhances file management through natural language prompts and semantic operations, improving user convenience and system capabilities.
paper_ai_keywords: LLMs LLM-based agents agent operating systems AIOS semantic file system LSFS semantic file management API set semantic file retrieval file update monitoring summarization semantic file rollback semantic indexes vector database CRUD group by join content summarization version comparison