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Originally Posted by kiwidude
I would second the "dont bother with goodreads involvement" when it comes to tags/genres.
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answered above. I used something I felt was clean code (kudos again), since it was in the midst of doing the 'right things'. I didn't have to figure out how to get the data, then process the data, then save the data, I could just 'slide in' and add my desired tags, and use the results from goodreads as my base info
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I'm also confused as to what your LLM is actually doing - you give it tags and it gives tags back? That seems a bit of a strange thing to try to be doing - surely that is just garbage in, garbage out? Kovid's suggestions seem more like what I too would think an LLM would be used for - a case of "tell me something I don't know", not "something I know already".
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No, to clarify (it was late when I wrote this)...
currently (and I am refining improving this based on results from my use of the current):
I prompt the LLM with
"Classify the following book with one or more of the following tags:
[desired 'broad' tag list here, mostly major genres from GR, but modified as desired by me, to sort appropriately].
Title: [title]
Description: [Description if any from GR]"
I then take the results, which might be more of less formatted but depending on the LLM to be consistent is unreliable, and while I could force it to a json result, it's just easier:
python: given [tag list], parse results looking for any/all tags returned.
Use that set plus the GR tags, and return the lot of them as result.
Next gen (and this will be settings-able), the prompt will be adjustable, along with url/etc.
I expect my refined prompt will be something along the lines of:
"Given the following book information, respond with the following answers:
1) Fiction or Non-Fiction
2) Reader Audience: Childrens, Young Adult, Normal Adult, Reference, Textbook
3) one or more of the following genres:
Science Fiction
Fantasy
Romance
Paranormal
Western
Mystery
Thriller
... etc...
"
Not one of the existing metadata sources (and as I said GR is perhaps the best to date, Amazon and others are poor seconds at best) does this well enough for my purposes.
LLM have lots of limits, but given Text Info, and asking it to classify into various buckets? That's usually a good fit. Not asking it to read the book, just scan the 'back cover' and sort. Basically robotic bookshelf clerking.