The Design of an LLM-powered Unstructured Analytics System
<|reference_start|>The Design of an LLM-powered Unstructured Analytics System: LLMs demonstrate an uncanny ability to process unstructured data, and as such, have the potential to go beyond search and run complex, semantic analyses at scale. We describe the design of an unstructured analytics system, Aryn, and the tenets and use cases that motivate its design. With Aryn, users can specify queries in natural language and the system automatically determines a semantic plan and executes it to compute an answer from a large collection of unstructured documents using LLMs. At the core of Aryn is Sycamore, a declarative document processing engine, built using Ray, that provides a reliable distributed abstraction called DocSets. Sycamore allows users to analyze, enrich, and transform complex documents at scale. Aryn also comprises Luna, a query planner that translates natural language queries to Sycamore scripts, and the Aryn Partitioner, which takes raw PDFs and document images, and converts them to DocSets for downstream processing. Using Aryn, we demonstrate a real world use case for analyzing accident reports from the National Transportation Safety Board (NTSB), and discuss some of the major challenges we encountered in deploying Aryn in the wild.<|reference_end|>
@article{anderson2024the,
    title={The Design of an LLM-powered Unstructured Analytics System},
    author={Eric Anderson, Jonathan Fritz, Austin Lee, Bohou Li, Mark Lindblad,
  Henry Lindeman, Alex Meyer, Parth Parmar, Tanvi Ranade, Mehul A. Shah,
  Benjamin Sowell, Dan Tecuci, Vinayak Thapliyal, Matt Welsh},
    journal={arXiv preprint arXiv:2409.00847},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00847},
    primaryClass={cs.DB cs.AI cs.IR}
}