Building a search tool and classifying text using NLP and ML
Here are a few general thoughts about your project:

As far as I understand, you're trying to extract very specific information from a semi-structured database using free-form natural language queries, correct? If yes it's important that you realize that this is a quite ambitious project, reaching a decent stage of quality is probably going to take a lot of work, and the performance is unlikely to be perfect.

Apparently numerical values and units are important pieces of information for matching the query. In this case you should probably implement a special process for those, because standard text processing is not going to work very well.


Detection: if there is only a small number of possible ways in which these values are written, it's probably more efficient to use ad-hoc regular expressions. If not, you could try to train a custom NER model.
Representation: that's the tricky part imho. For general text vectors are fine, but with vectors it's difficult/impossible to represent special values like those. Given that you have a semi-structured database, you might want to try a more semantic representation adapted to your data: that could involve techniques from semantic role labeling, relationship extraction, etc.
Matching: the advantage of a semantic representation is that you could convert the query to a semantic representation and then apply a detailed matching procedure suited to your data (in particular you can compare corresponding numerical values and use a threshold, or return the difference to represent how far they are).