Metacat

cache the imported models to avoid timeouts from remote hosts (or being locked out for too many requests in a given time period).

process all the returned annotation suggestions until we find one that is appropriately located in the subclass hierarchy for the given superclass.

use in-memory TDB dataset for querying annotations for indexing -- this comes with the same reasoning capabilities as the directory-based one, but has the benefit of not filling the directory with triples that will not be used again. prepping for d1 AHM

pass around the object file path rather than the data stream so that multiple subprocessors can index the same object and not consume the stream before it gets to the next one. In preparation for extending the assertions stored in OREs. https://projects.ecoinformatics.org/ecoinfo/issues/6548

when indexing annotations directly, just use an in-memory triple store rather than TDB since we remove each graph as it is processed (and my TDB instance would get into the multi-GB range with a few runs, even if I removed the old models)

redirect "short form" metacat read URIs to the the new Metacat UI using the configured UI context. This translates the docid -> pid to use the correct identifier for the correct service. https://projects.ecoinformatics.org/ecoinfo/issues/6546

simplify lookup for classes and orcid. remove the "random" annotation code branches -- just too confusing to look at those bogus classes especially now that we have "real" generated annotations.

add 'test' for indexing annotations without actually storing the RDF of the generated annotation.

only allow multiple values for multi-valued fields....

Add admin service to update DOI registrations by specifying a list of formatIds or DOIs, or update all.