The following webinar "Introduction to NLP models and vector search" gives a very nice demo on uploading and using NLP models to improve search relevance. However, it might seem it is only focused on core Elasticsearch:
I am wondering can that be integrated with Elastic Enterprise App Search ? Concretely, I would like to know how to integrate NLP models for semantic search into engines within App Search ?
Secondly, and putting things into context, I currently have an engine for an entity
Courses which I populate through the API, I later use the Search UI provided by App Search which behind the scenes uses the API
/api/as/v1/engines/courses/search.json: results would then reflect the relevance configuration done in the engine. I would like to know then: what is the approach to mix this regular engine search and the NLP
_knn_search search showcased in the demo if we are building a UI that should benefit from both to improve relevance ? How do they play along together ?
Thanks in advance.