PyTerrier provides a declarative framework for building and experimenting with Information Retrieval (IR) pipelines. In this demonstration, we highlight several recent pipeline operations that improve their ability to be programmatically inspected, visualized, and integrated with other tools (via the Model Context Protocol, MCP). These capabilities aim to make it easier for researchers, students, and AI agents to understand and use a wide array of IR pipelines.

翻译：PyTerrier为构建和实验信息检索（IR）流水线提供了一个声明式框架。在本演示中，我们重点介绍了几种近期开发的流水线操作，这些操作提升了流水线在程序化检查、可视化以及通过模型上下文协议（MCP）与其他工具集成方面的能力。这些功能旨在让研究人员、学生和AI智能体更易于理解和使用多样化的信息检索流水线。