ArcNeural: A Multi-Modal Database for the Gen-AI Era

TL;DR

ArcNeural presents a comprehensive multimodal database system optimized for Generative AI and Large Language Models, integrating diverse data types with high performance and scalability for enterprise AI applications.

Contribution

It introduces a unified storage and processing architecture combining graph, vector, and document data management tailored for Gen AI needs.

Findings

Demonstrates superior performance over existing systems

Supports real-time analytics for multimodal data

Offers scalable and versatile data management solutions

Abstract

ArcNeural introduces a novel multimodal database tailored for the demands of Generative AI and Large Language Models, enabling efficient management of diverse data types such as graphs, vectors, and documents. Its storage-compute separated architecture integrates graph technology, advanced vector indexing, and transaction processing to support real-time analytics and AI-driven applications. Key features include a unified storage layer, adaptive edge collection in MemEngine, and seamless integration of transaction and analytical processing. Experimental evaluations demonstrate ArcNeural's superior performance and scalability compared to state-of-the-art systems. This system bridges structured and unstructured data management, offering a versatile solution for enterprise-grade AI applications. ArcNeural's design addresses the challenges of multimodal data processing, providing a robust framework for intelligent, data-driven solutions in the Gen AI era.