Combining Elasticsearch And Semantic Search: A Case Study (Part 1)
Search engines are becoming smarter, more intuitive, and more responsive to user needs. Among the variety of tools and techniques available, Elasticsearch has established itself as a powerful platform for lightning-fast search operations. But what if we could further amplify its capabilities?
In this article, we embark on an exploratory journey to meld the precision of Elasticsearch with the nuanced understanding of semantic search.
Through a hands-on case study, we’ll scrutinize the outcomes of this blend and evaluate its efficacy. From initial successes to baffling challenges, our experiments offer insights into the promises and pitfalls of integrating traditional keyword search with cutting-edge semantic algorithms.
Dive in as we decode the intricacies of modern search and lay the groundwork for the next wave of innovation in information retrieval.
Basic Keyword Search
Let’s begin by exploring the foundation of most search systems: the Basic Keyword Search.
This conventional method, which relies heavily on exact keyword matches, has been the bedrock of information retrieval for years. But how does it fare in real-world scenarios? And more importantly, what potential lies beyond it?
