This could be seriously addressed with a configurable rule system similar to email filters + search. You have to store any of the metadata factors you want to consider in a companion index that can allow complex filtering or decision tree splits, then for introspection of SQL-like data sources, you can follow key and type relationships to determine what’s joinable.
Perhaps outputting several potential answers at the end, each explaining the “pathway” it chose to use (filters / decision tree splits + graphical path through keys / joinable types in the underlying data), and allow the user to select one or more results that they believe are valid pathways of criteria, or perhaps tweak individual filters and joins in the listed pathway for a given result.
I think this would offer a lot more value than trying to get a full natural language interface that “just works” on complex filtering conditions, where getting just one answer back (instead of seeing the variety of pathways the system could choose and what influence each step has on the end result) entails too many cases the ML system fails with unrealistic results.
Perhaps outputting several potential answers at the end, each explaining the “pathway” it chose to use (filters / decision tree splits + graphical path through keys / joinable types in the underlying data), and allow the user to select one or more results that they believe are valid pathways of criteria, or perhaps tweak individual filters and joins in the listed pathway for a given result.
I think this would offer a lot more value than trying to get a full natural language interface that “just works” on complex filtering conditions, where getting just one answer back (instead of seeing the variety of pathways the system could choose and what influence each step has on the end result) entails too many cases the ML system fails with unrealistic results.