Elastic continues to help everyone find what they need faster with Elastic Enterprise Search, a comprehensive solution for building search-powered applications. Elastic Enterprise Search elevates relevance and precision at scale with a new combination of traditional and machine learning-assisted techniques.
Some time ago, AWS forked ElasticSearch, the most popular search engine on the planet. They had some struggles with the maintainer of ElasticSearch and decided it was time to part ways. So, with OpenSearch, there is now a new kid in town. Well, not new, but at least some kind of alternative.
OK, so you want to sort your documents by something that can’t be implemented with Solr’s built-in functions. This calls for a custom function, which you can implement through your own ValueSourceParser. To address the elephant in the room, Elasticsearch and OpenSearch have script sorting. This is easier to implement, but not as close to Lucene. Though of course you can use a native script as well.
In Enterprise Search 8.4, hybrid ranking for vector similarity is now available from the main querying endpoint, commonly known as the _search Elasticsearch endpoint. Introduced as a standalone query endpoint in 8.0, vector querying functionality (specifically, kNN vector similarity) greatly simplifies and accelerates the process of crafting and issuing queries that leverage the native vector querying capabilities of Elasticsearch along with the tried-and-tested traditional scoring algorithms.
Your search application is more powerful than you realize. With these features, you can harness search data to build a better customer experience. What’s the top thing customers want when purchasing online? It’s ease. Experiencing friction for even a fraction of a second may send a shopper to a competitor’s site. It may also mean they don’t return to your site the next time they’re looking to purchase.