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.

翻译：得益于生成式架构与大型语言模型的进步，数据科学家如今能够编写机器学习操作流水线来处理海量非结构化数据。近期进展中，声明式人工智能框架（如Palimpzest、Lotus与DocETL）的兴起使得构建优化且日益复杂的流水线成为可能，但这些系统通常仍仅限专业程序员使用。在本演示中，我们推出PalimpChat——一个基于聊天界面的Palimpzest交互系统，通过允许用户仅使用自然语言创建并运行复杂的人工智能流水线，成功弥合了这一鸿沟。通过整合基于ReAct的推理智能体Archytas以及Palimpzest的关系型与基于LLM的操作符套件，PalimpChat生动展示了聊天界面如何使声明式人工智能框架真正惠及非专业用户。我们的演示系统已公开在线提供。在SIGMOD'25会议上，参与者可探索三个真实应用场景——科学发现、法律取证与房地产搜索，或将PalimpChat应用于自有数据集。本文重点阐述PalimpChat在Palimpzest优化器支持下，如何简化诸如生物医学数据提取与分析等复杂人工智能工作流。