We’re excited to share a recent enhancement made to the Elastic Support Hub: it’s now powered by semantic search!
But before we go into more detail on the changes we made to the Elastic® Support Hub and its impact on our customers, it's important that we take a moment to explain the concept of semantic search. At its core, semantic search is a method of search that uses AI to return more relevant search results. Take a look at this quick video explaining the concept:
As shown in the video, semantic search matches the intent of what the user searches to the content available rather than the words. You can read more about the AI behind it on our blog, Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search. The rest of this blog tells our story about moving the Elastic Support Hub to semantic search.
Why did we make this change?
All technology news these days seems to have something to do with large language models and generative AI. Elastic is leading the charge with its vector database capabilities and built-in natural language models. It makes sense that we should build our supporting applications on the same bleeding edge that our product lives on. By making this change now, we can provide feedback to our product development teams and make the product better for everyone.
Biggest takeaway configuring semantic search
As with most new technology innovations, it requires tearing down, replacing older code, and potentially updating underlying architecture. Our internal app development team faced these challenges head-on, and we are now in a much better position to iterate on any of Elasticsearch®’s new features. From our teams' point of view, there were two significant features that stood out in the setup process:
1. Considering ELSER, Elastic’s proprietary transformer model for semantic search, is a relatively new feature in Elasticsearch (8.8), our development team was happy to see a guided UI experience to enable Elasticsearch ingest pipelines with ELSER.