Search functionality has evolved significantly over time, from the 1950s with the development of inverted indexes to leverage efficient information retrieval across large databases. Vector search represents a significant advancement in search capabilities, enabling faster and more accurate results by understanding both queries and documents through semantic representation. Recent years have seen rapid innovation in large language models (LLMs), which can be used to address challenges such as stemming, synonyms, and autocorrect, making vector search more accessible and low maintenance. However, keyword search still has its advantages, particularly for simple and known queries, and an ideal system would benefit from combining both functionalities to ensure fast, relevant, and accurate results.