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流水线提供了一个声明式框架。本演示重点介绍了近期开发的若干流水线操作，这些操作增强了流水线在程序化检查、可视化以及与其他工具集成（通过模型上下文协议MCP）方面的能力。这些功能旨在帮助研究人员、学生和AI智能体更轻松地理解和使用各类信息检索流水线。