This survey explores the synergistic potential of Large Language Models (LLMs) and Vector Databases (VecDBs), a burgeoning but rapidly evolving research area. With the proliferation of LLMs comes a host of challenges, including hallucinations, outdated knowledge, prohibitive commercial application costs, and memory issues. VecDBs emerge as a compelling solution to these issues by offering an efficient means to store, retrieve, and manage the high-dimensional vector representations intrinsic to LLM operations. Through this nuanced review, we delineate the foundational principles of LLMs and VecDBs and critically analyze their integration's impact on enhancing LLM functionalities. This discourse extends into a discussion on the speculative future developments in this domain, aiming to catalyze further research into optimizing the confluence of LLMs and VecDBs for advanced data handling and knowledge extraction capabilities.

翻译：本综述探讨了大语言模型（LLMs）与向量数据库（VecDBs）之间的协同潜力，这是一个新兴但快速发展的研究领域。随着LLMs的普及，一系列挑战也随之而来，包括幻觉、知识过时、商业应用成本过高以及记忆问题。VecDBs通过提供一种高效存储、检索和管理LLM操作固有的高维向量表示的方法，成为解决这些问题的有力方案。通过这篇细致的综述，我们阐述了LLMs和VecDBs的基本原理，并批判性分析了二者集成对增强LLM功能的影响。本文进一步探讨了该领域未来可能的发展方向，旨在推动更多研究以优化LLMs与VecDBs的结合，从而提升数据处理和知识提取能力。