I've used both of these in my current role to make substantial improvements to our Solr search engine.
They include a good range of techniques between "quick wins you could implement and test in an hour" and "complex machine learning pipelines based on millions of data points".
AI Powered Search was probably the more interesting and useful but it's also a bit of a misnomer. Half of the techniques aren't related to AI (which is fine) and the half that are, are rapidly out of date. Semantic/vector search is now miles ahead of what the book talks about, with dense vector support in Solr/Elastic/Opensearch; sparse models; hybrid search/RRF... but I digress :)
If you're interested in how to improve the magic black box that is search, they're worthwhile reads.
Just remember that expectations are everything. There are no two books, or twenty books, that'll turn your out-of-the-box Solr instance into Google or Bing quality. But you can end up with a magic black box that serves much better results, which is nice!