From Elasticsearch Bottlenecks to Weaviate: How We Built Fast Hybrid Search in Production
The article discusses the transition from using Elasticsearch to Weaviate for hybrid search capabilities. It highlights the limitations of traditional keyword search and the need for a system that combines exact keyword matching with semantic understanding. The author shares insights on the challenges faced while trying to adapt Elasticsearch for these requirements and the eventual shift to Weaviate.
- ▪Elasticsearch is a powerful tool for full-text search and filtering but has limitations for hybrid search needs.
- ▪The author faced challenges when trying to make Elasticsearch function as a vector search engine, particularly with score fusion.
- ▪Weaviate was chosen as a solution to effectively combine exact keyword matching with semantic search capabilities.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 161199) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } amir Posted on Jun 3 From Elasticsearch Bottlenecks to Weaviate: How We Built Fast Hybrid Search in Production #search #weaviate #elasticsearch #go For years, Elasticsearch was one of those tools I would almost automatically reach for whenever a system needed search. And honestly, for many use cases, it is still excellent. If you need full-text search, filtering, aggregations, faceting, observability queries, or log exploration, Elasticsearch is a very mature and powerful engine.
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