PalimpChat: Declarative and Interactive AI analytics
Name
3722212.3725122.pdf
Size
1.21 MB
Format
Adobe PDF
Checksum (MD5)
55ebed8073adc2edb06244bd90528d72
Author(s)
Liu, Chunwei
Vitagliano, Gerardo
Rose, Brandon
Printz, Matthew
Samson, David Andrew
Cafarella, Michael
Date Issued
June 22, 2025
Publisher
ACM|Companion of the 2025 International Conference on Management of Data
Citation
Chunwei Liu, Gerardo Vitagliano, Brandon Rose, Matthew Printz, David Andrew Samson, and Michael Cafarella. 2025. PalimpChat: Declarative and Interactive AI analytics. In Companion of the 2025 International Conference on Management of Data (SIGMOD/PODS '25). Association for Computing Machinery, New York, NY, USA, 183–186.
Version
Final published version
Abstract
Thanks to the advances in generative architectures and large language models, data scientists can now code pipelines of AI operations to process large collections of unstructured data. Recent progress has seen the rise of declarative AI frameworks (e.g., Palimpzest, Lotus, and DocETL) to build optimized and increasingly complex pipelines, but these systems often remain accessible only to expert programmers. In this demonstration, we present PalimpChat, a chat-based interface to Palimpzest that bridges this gap by letting users create and run sophisticated AI pipelines through natural language alone. By integrating Archytas, a ReAct-based reasoning agent, and Palimpzest's suite of relational and LLM-based operators, PalimpChat provides a practical illustration of how a chat interface can make declarative AI frameworks truly accessible to non-experts.
Our demo system is publicly available online. At SIGMOD'25, participants can explore three real-world scenarios-scientific discovery, legal discovery, and real estate search-or apply PalimpChat to their own datasets. In this paper, we focus on how PalimpChat, supported by the Palimpzest optimizer, simplifies complex AI workflows such as extracting and analyzing biomedical data.
Description
SIGMOD-Companion ’25, Berlin, Germany
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3722212.3725122
