LLM-Based Semantic File System for AIOS: From Commands to Prompts

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Large language models (LLMs) have revolutionized the way we interact with computers, but the underlying file systems remain stuck in the traditional paradigm of navigating through complex folder hierarchies using cryptic commands. This poses a bottleneck to the usability of LLM-based applications and systems, like AI agents and agent operating systems (AIOS), as users are required to remember precise commands and struggle to navigate complex file structures.

To address this limitation, a new paper, “From Commands to Prompts: LLM-based Semantic File System for AIOS,” proposes an innovative LLM-based semantic file system (LSFS) that allows users to manage files through natural language prompts. Unlike conventional approaches, LSFS leverages the semantic meaning of files, enabling users to interact with them in a more intuitive and human-friendly manner.

The paper’s key contributions include:

• Semantic file storage and indexing: Instead of relying solely on file attributes (like name, size, and timestamps), LSFS incorporates semantic information extracted from the content of files using embedding models and vector databases. This enables users to search for files based on their content rather than just their names or locations.
• LLM-based parser: LSFS integrates an LLM-based parser that transforms natural language prompts into executable API calls. This allows users to manage files using simple instructions like, “Summarize the changes in File A after updating it with File B,” or “Find all papers related to LLM Uncertainty from folder named example.” The parser leverages LLMs to extract keywords and context from user prompts, enabling seamless execution of file management tasks.
• Comprehensive API set: The paper presents a set of APIs that support semantic file management functionalities, such as semantic file retrieval, file update monitoring and summarization, and semantic file rollback.
• Systematic safety mechanisms: To prevent unintended operations, LSFS incorporates systematic safety insurance mechanisms like safety checks for irreversible operations and user verification before executing commands.
• Performance evaluation: Extensive experiments demonstrate that LSFS outperforms traditional file systems in terms of user convenience, the diversity of supported functions, and the accuracy and efficiency of file operations. The paper also shows that LSFS maintains good performance in non-semantic file management tasks, such as keyword-based file retrieval and file sharing.

For example, imagine a user wants to find all papers related to “LLM uncertainty” from a specific folder. With a traditional file system, they would have to remember the exact folder location and file names, then use commands like grep to search for the keyword. With LSFS, the user can simply type, “Find all papers related to LLM Uncertainty from folder named example,” and LSFS will leverage its semantic indexing and retrieval capabilities to identify and return the relevant files.

The paper’s proposed LSFS is a significant step toward building intelligent and user-friendly operating systems. Its innovative approach to semantic file management has the potential to revolutionize the way we interact with files, making it easier and more efficient to manage and utilize information.

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