PalimpChat: Declarative and Interactive AI analytics
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| Format: | Preprint |
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2025
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| _version_ | 1866915139976953856 |
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| author | Liu, Chunwei Vitagliano, Gerardo Rose, Brandon Prinz, Matt Samson, David Andrew Cafarella, Michael |
| author_facet | Liu, Chunwei Vitagliano, Gerardo Rose, Brandon Prinz, Matt Samson, David Andrew Cafarella, Michael |
| contents | Thanks to the advances in generative architectures and large language models, data scientists can now code pipelines of machine-learning 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_03368 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PalimpChat: Declarative and Interactive AI analytics Liu, Chunwei Vitagliano, Gerardo Rose, Brandon Prinz, Matt Samson, David Andrew Cafarella, Michael Artificial Intelligence Databases Information Retrieval Thanks to the advances in generative architectures and large language models, data scientists can now code pipelines of machine-learning 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. |
| title | PalimpChat: Declarative and Interactive AI analytics |
| topic | Artificial Intelligence Databases Information Retrieval |
| url | https://arxiv.org/abs/2502.03368 |