Hybrid Semantic Search: Unveiling User Intent Beyond Keywords

TL;DR

This paper presents a hybrid semantic search system that combines keyword matching, embedding models, and LLMs to better understand user intent and improve search relevance and accuracy.

Contribution

It introduces a novel hybrid search approach integrating multiple methods to capture explicit and implicit user intent more effectively.

Findings

Enhanced search relevance and accuracy demonstrated.

Faster query execution techniques developed.

Effective capture of user intent beyond keywords.

Abstract

This paper addresses the limitations of traditional keyword-based search in understanding user intent and introduces a novel hybrid search approach that leverages the strengths of non-semantic search engines, Large Language Models (LLMs), and embedding models. The proposed system integrates keyword matching, semantic vector embeddings, and LLM-generated structured queries to deliver highly relevant and contextually appropriate search results. By combining these complementary methods, the hybrid approach effectively captures both explicit and implicit user intent.The paper further explores techniques to optimize query execution for faster response times and demonstrates the effectiveness of this hybrid search model in producing comprehensive and accurate search outcomes.