RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines

TL;DR

This paper introduces RAGOps, a framework for managing retrieval-augmented generation systems by addressing data management, lifecycle, and quality challenges to improve enterprise LLM applications.

Contribution

It characterizes RAG architecture, outlines management lifecycle, and defines design considerations for operationalizing RAG systems in production environments.

Findings

Characterized RAG architecture using 4+1 model view

Outlined RAG system lifecycle integrating data and model management

Presented two RAG application use cases with operational considerations

Abstract

Recent studies show that 60% of LLM-based compound systems in enterprise environments leverage some form of retrieval-augmented generation (RAG), which enhances the relevance and accuracy of LLM (or other genAI) outputs by retrieving relevant information from external data sources. LLMOps involves the practices and techniques for managing the lifecycle and operations of LLM compound systems in production environments. It supports enhancing LLM systems through continuous operations and feedback evaluation. RAGOps extends LLMOps by incorporating a strong focus on data management to address the continuous changes in external data sources. This necessitates automated methods for evaluating and testing data operations, enhancing retrieval relevance and generation quality. In this paper, we (1) characterize the generic architecture of RAG applications based on the 4+1 model view for describing software architectures, (2) outline the lifecycle of RAG systems, which integrates the management lifecycles of both the LLM and the data, (3) define the key design considerations of RAGOps across different stages of the RAG lifecycle and quality trade-off analyses, (4) highlight the overarching research challenges around RAGOps, and (5) present two use cases of RAG applications and the corresponding RAGOps considerations.