PalimpChat：声明式与交互式人工智能分析
PalimpChat: Declarative and Interactive AI analytics
AI总结：
PalimpChat 通过自然语言聊天接口结合 Archytas 推理智能体和 Palimpzest 优化器，使非专家用户也能创建和运行复杂的声明式 AI 分析流水线。
摘要：
得益于生成式架构和大语言模型的进步，数据科学家现在可以编写机器学习操作流水线来处理大规模非结构化数据集合。近期进展见证了声明式人工智能框架（如 Palimpzest、Lotus 和 DocETL）的兴起，用于构建优化且日益复杂的流水线，但这些系统通常仍仅对专业程序员开放。在本演示中，我们展示了 PalimpChat，一个基于聊天的 Palimpzest 接口，通过让用户仅使用自然语言即可创建和运行复杂的人工智能流水线，弥合了这一差距。通过集成 Archytas（一个基于 ReAct 的推理智能体）以及 Palimpzest 的关系型和基于大语言模型的算子套件，PalimpChat 提供了一个实际示例，说明聊天接口如何使声明式人工智能框架真正为非专家用户所用。我们的演示系统已在线公开提供。在 SIGMOD'25 上，参与者可以探索三个真实场景——科学发现、法律发现和房地产搜索——或将 PalimpChat 应用于自己的数据集。在本文中，我们重点介绍 PalimpChat 如何在 Palimpzest 优化器的支持下，简化复杂的 AI 工作流，例如生物医学数据的提取和分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
