Patchwork: A Unified Framework for RAG Serving

TL;DR

Patchwork is a comprehensive framework that optimizes the deployment and management of Retrieval Augmented Generation systems, significantly improving efficiency, scalability, and reliability through flexible design and dynamic scheduling.

Contribution

It introduces a unified, end-to-end RAG serving framework with customizable pipelines, distributed inference deployment, and online scheduling for improved performance and reliability.

Findings

Achieves over 48% throughput improvement

Reduces SLO violations by approximately 24%

Demonstrates effectiveness across four RAG implementations

Abstract

Retrieval Augmented Generation (RAG) has emerged as a new paradigm for enhancing Large Language Model reliability through integration with external knowledge sources. However, efficient deployment of these systems presents significant technical challenges due to their inherently heterogeneous computational pipelines comprising LLMs, databases, and specialized processing components. We introduce Patchwork, a comprehensive end-to-end RAG serving framework designed to address these efficiency bottlenecks. Patchwork's architecture offers three key innovations: First, it provides a flexible specification interface enabling users to implement custom RAG pipelines. Secondly, it deploys these pipelines as distributed inference systems while optimizing for the unique scalability characteristics of individual RAG components. Third, Patchwork incorporates an online scheduling mechanism that continuously monitors request load and execution progress, dynamically minimizing SLO violations through strategic request prioritization and resource auto-scaling. Our experimental evaluation across four distinct RAG implementations demonstrates that Patchwork delivers substantial performance improvements over commercial alternatives, achieving throughput gains exceeding 48% while simultaneously reducing SLO violations by ~24%.