Private Microservice with Retrieval-Augmented Generation and Embedded LLM

Abstract

This research introduces a modern private microservice architecture that integrates Retrieval-Augmented Generation and embedded local execution of Large Language Models based on MLX-LM, specifically optimised for Apple Silicon (M-series) hardware. By embedding RAG pipelines and LLM inference directly into a unified local microservice, the system achieves enhanced data confidentiality, private data, and embedding storage compared to conventional cloud based deployments. Performance evaluations demonstrate that the architecture efficiently manages computational resources, maintaining availability even under intense workloads. Leveraging Apple’s MLX framework, the solution attains great inference speed, reduced power consumption, and flexible deployment workflows. This study provides a practical foundation and clear guidelines for deploying secure, efficient, and privacy-centric AI microservices on local Apple Silicon infrastructure, highlighting significant opportunities for future research and industrial adoption.