Integration of Vector Databases with Large Language Models (LLMs)

The fast-accelerating growth of unstructured and semi-structured data leads traditional relational and NoSQL databases to unsatisfactory performance of today’s queries system.This paper explores the advantages of using vector databases rather than using traditional databases for storing and maintaining the databases with Large Language Models(LLMs) to improve and increase the efficiency of the query retrieval.Vector embeddings are generated using Hugging Face models, while Google PaLM further refines the search process. To efficiently match queries, Locality Sensitive Hashing (LSH) is used. The results significantly demonstrates the improvements in query accuracy and retrieval speed, underlining the collaboration between vector databases and LLMs for data-intensive applications.