RoutIR: Fast Serving of Retrieval Pipelines for Retrieval-Augmented Generation
Eugene Yang, Andrew Yates, Dawn Lawrie, James Mayfield, Trevor Adriaanse

TL;DR
RoutIR is a Python package that enables fast, flexible, and online serving of complex retrieval pipelines for Retrieval-Augmented Generation systems, supporting dynamic configurations and multiple retrieval methods.
Contribution
The paper introduces RoutIR, a novel Python package that facilitates real-time, customizable retrieval pipelines with an easy-to-use HTTP API for RAG systems.
Findings
Supports arbitrary retrieval methods including reranking and query expansion.
Enables dynamic, on-the-fly construction of retrieval pipelines.
Provides asynchronous batching and caching for efficiency.
Abstract
Retrieval models are key components of Retrieval-Augmented Generation (RAG) systems, which generate search queries, process the documents returned, and generate a response. RAG systems are often dynamic and may involve multiple rounds of retrieval. While many state-of-the-art retrieval methods are available through academic IR platforms, these platforms are typically designed for the Cranfield paradigm in which all queries are known up front and can be batch processed offline. This simplification accelerates research but leaves state-of-the-art retrieval models unable to support downstream applications that require online services, such as arbitrary dynamic RAG pipelines that involve looping, feedback, or even self-organizing agents. In this work, we introduce RoutIR, a Python package that provides a simple and efficient HTTP API that wraps arbitrary retrieval methods, including first…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Semantic Web and Ontologies · Image Retrieval and Classification Techniques
