Technology Category
Cognitive Modeling & Cognitive Systems: Agent ArchitecturesMachine Learning: Hardware-aware MLData Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
AI Engineering Blueprint for On-Premises Retrieval-Augmented Generation Systems
🤖 AI Summary
This work addresses the challenge enterprises face in adopting cloud-based Retrieval-Augmented Generation (RAG) systems due to stringent data compliance requirements, compounded by the absence of a standardized reference architecture for on-premises deployment. To bridge this gap, we propose the first enterprise-grade, on-premises RAG engineering framework grounded in the 4+1 architectural view model. Our solution encompasses an end-to-end reference architecture, a deployable open-source reference application, a localized toolchain, and a customized CI/CD pipeline. This comprehensive framework fills a critical void in the standardized implementation of enterprise-scale, on-premises RAG systems. The complete blueprint and source code have been open-sourced on GitHub, and preliminary validation through industry partnerships and expert interviews demonstrates its practical utility and feasibility.
Technology Category
Application Category
📝 Abstract
Retrieval-augmented generation (RAG) systems are gaining traction in enterprise settings, yet stringent data protection regulations prevent many organizations from using cloud-based services, necessitating on-premises deployments. While existing blueprints and reference architectures focus on cloud deployments and lack enterprise-grade components, comprehensive on-premises implementation frameworks remain scarce.
This paper aims to address this gap by presenting a comprehensive AI engineering blueprint for scalable on-premises enterprise RAG solutions. It is designed to address common challenges and streamline the integration of RAG into existing enterprise infrastructure. The blueprint provides: (1) an end-to-end reference architecture described using the 4+1 view model, (2) a reference application for on-premises deployment, and (3) best practices for tooling, development, and CI/CD pipelines, all publicly available on GitHub. Ongoing case studies and expert interviews with industry partners will assess its practical benefits.
Problem
Research questions and friction points this paper is trying to address.
Retrieval-Augmented Generation
on-premises deployment
enterprise AI
data protection regulations
AI engineering blueprint
Innovation
Methods, ideas, or system contributions that make the work stand out.
on-premises RAG
enterprise AI architecture
4+1 view model
CI/CD for AI
retrieval-augmented generation
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