  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including hallucinations, outdated knowledge, prohibitive commercial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.
