Hybrid Semantic Search: Unveiling User Intent Beyond Keywords

At its core, semantic search hinges This paper addresses the limitations of on two crucial components. The first, the search traditional keyword-based search in function, acts similarly to traditional search understanding user intent and introduces a engines [1] by identifying and ranking novel hybrid search approach that leverages documents relevant to a user's query within a the strengths of non-semantic search engines, vast collection of information (corpus). However, Large Language Models (LLMs), and semantic search goes beyond this basic embedding models. The proposed system functionality with its second component: integrates keyword matching, semantic vector semantic understanding. This is where embeddings, and LLM-generated structured Transformers come into play, allowing the queries to deliver highly relevant and system to delve deeper than keyword matching.