Vector search beyond the database

Vector search technology enhances retrieval quality by incorporating semantics, allowing for fuzzy matching of query meanings to content meanings, thus improving recall and relevance in information retrieval. However, vector search alone has limitations, especially where precision is crucial, leading to the industry's adoption of hybrid models that combine text and vector search, utilizing tensor math and machine-learned models for scoring and relevance. This approach necessitates a different architecture from traditional databases, as effective vector search requires a focus on ranking rather than storage. The relevance is particularly critical in Retrieval-Augmented Generation (RAG) applications for Large Language Models (LLMs), as these models rely entirely on the precision of retrieved information to perform tasks. While databases with vector support might seem convenient for organizations, achieving high-quality results typically demands a dedicated vector-enabled search engine to handle the complex relevance work, suggesting a strategic separation based on the quality requirements of specific use cases.