Pipeline Inspection, Visualization, and Interoperability in PyTerrier

TL;DR

PyTerrier offers a declarative IR pipeline framework with enhanced inspection, visualization, and interoperability features, facilitating research, education, and integration with other tools.

Contribution

The paper introduces recent pipeline operations in PyTerrier that improve programmatic inspection, visualization, and integration capabilities for IR pipelines.

Findings

Enhanced pipeline inspection and visualization features

Implementation of Model Context Protocol (MCP) for interoperability

Facilitates understanding and use of IR pipelines

Abstract

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.