Elastic Support Hub moves to semantic search

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.

This allowed our developers to quickly add the necessary text expansion configuration to the ingestion pipeline that makes semantic search possible. This made the configuration experience much easier to get started and see results quicker.

2. A machine learning model like ELSER takes dedicated machine resources to run (minimum 4GB). Since we were already running on Elastic Cloud, we were able to enable dedicated machine learning (ML) nodes with autoscaling to accommodate our resource demands and see more consistent performance.  

While there isn’t a single article that explains all the ways you can index data (there are a lot), you can see how fundamentally different these results are. For full-text search, we have a mix of guides, troubleshooting articles, and a blog with matched keywords, but none of them answer the question of “how.” Or to say it differently, text search didn't capture the meaning (semantically) of the query and did its best to match keywords.

For the semantic search results, you can see blogs that generally relate to the indexing of data. What is even more interesting is the fourth returned result of “How to ingest data into Elasticsearch Service” as the term ingest is actually more relevant to the process of adding data to an index. Elastic’s out-of-the-box transformer model picked up on the semantic meaning of adding data to an index and returned more relevant results regardless of the exact keywords.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

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