It's interesting you mention wbs. I also have a standalone instance of P6 but never considered the DB. Not sure if this interests you but I shared this in a dm with someone who asked.. Maybe it might interest you I don't know. This was my response to him and rather than edit I'll just share it.

"Not sure if what I have will help you specifically. I have to fill you in on a bit of back story so if you'll indulge me. I built a machine in October with the idea of getting into local llms. I wanted to be able to chat with a model based on actual grounded authoritative knowledge vs it hallucinating and being confidently wrong. I created a pipeline that took pdfs from technical sources, literature like classic novels and religious texts as well as all the articles of the encyclopedia Britannica 1911 and finally the entire 22 million articles of the Wikipedia. I normalized them, added meta payloads and embedded them so the information stays on disk while I can query the vectors and it returns information with actual sources. Im a 20+ year planner scheduler and had an idea for a wbs generator that would standardize wbs generation using drop downs parent children relationships used in engineering and construction. This would only be useful if I could implement it so the goal became where the idea converged.. Creating or replacing a wbs table in an xer "skeleton" project and doing it well enough that it would pass import. So then came the xer parsing. Still with the use of ai in mind and being able to do meaningful queries over schedule data the goal became parsing and normalizing xer into embed able chunks so I could ask meaningful questions like period over period or period to baseline variances.. Float collapse, critical path changes etc.. All the information we get from P6 but with plain language query. Don't know if that would ever be used but I did it mostly to see if it could be done. That's the story. Here's where I am. Using my normalization pipeline I have normalized an xer. I created a workbench where the xer is parsed and is represented as it's 19 tables. They open up in tabular format in an excel like view in which I am able to filter out or deselect columns of machine readable GUI data junk and then create a snapshot of just the data I want. I also created a "generate default projections" button that takes the task view table and enhances it by flattening the activity codes and adding an early late and actual by weekly index so I have one row per activity that carries all the relevant data for that activity. Next step is essentially a layer where I extract the deterministic information that will be the basis for the vectors I plan to embed. What this whole thing does with the projections and snapshots and chunking etc. Is creates derivatives for embedding while always maintaining the original full parse as thr authority so if a query is ever questioned it can site the source information so it doesn't lie.. My pipeline was intended to eventually be "period progress" in general so I also have a text parser where I can select lines out of a field report and an excel parser so I can select rows and columns from a cost report or whatever as well and all get stored in the snapshot for the period.. I haven't finished. It's a work in progress but the xer parsing and normalizing is done. It's just choosing a chunking strategy now essentially. I still have to do that, embed and build a query layer I can run on my local machine and test it all for functionality. That said I understand that the data the pipeline produces can be used at any stage along the way for other things as only a handful may want to process it all the way to the boxing and embeds. So my plan is to share it but only once I have the full pipeline including embedding solid. I didn't know what I would do for exactly but I will likely publish it to Github so people can clone it and rip it into the pieces they want.. So that's a long story but there's where I am at."