The Role of Vector Databases in Retrieval-Augmented Generation (RAG) for Improving Large Language Models (LLMs)

With the rapid advancement of large language models (LLMS), enhancing their accuracy and ensuring their up-to-date knowledge have emerged as significant challenges. The Retrieval- Augmented Generation (RAG) approach tackles these challenges by integrating information retrieval with language generation capabilities. Central to this process are vector databases, which play a critical role in storing, retrieving, and managing both structured and unstructured data, thereby enhancing the performance of RAG systems. This study employs a narrative review methodology to explore the role of vector databases in the RAG framework, demonstrating how these technologies facilitate the rapid and accurate retrieval of relevant information, ultimately improving the output quality of LLMs. Additionally, the study examines the challenges and opportunities associated with the use of vector databases in this context. The findings highlight that the integration of vector databases with RAG not only enhances the accuracy and updateability of language models but also paves the way for the development of more intelligent and responsive systems.

کلیدواژه ها:

Vector Databases ، Retrieval- Augmented Generation (RAG) ، Large Language Models (LLMs) ، Natural Language Processing (NLP) ، Information Retrieval Systems