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Possibly having existing tags be passed with the content, and have the AI api return any that 'could' apply, and new ones if 'none' apply. This way it does not just always come up with a new one, even if the same thing already exists. For example, mine has these two tags, that are exactly the same thing:
AI
Artificial Intelligence
If we passed the existing 'Artificial Intelligence' tag, it would have chosen it, and not created 'AI'
This seems to be a popular request, so i'll probably have to implement it at some point. The main problem though is that the naive implementation will be expensive if you have a lot of tags. Basically, the naive implementation is that you pass all the tags of the user to openai/ollama on every request and ask it to only select from those tags. While this is easy to implement, every word you add to the AI request basically costs more money. So if you have 1000 tags for example, and every article you add is around 1000 words, you'll end up paying twice as much per inference request. I'm happy to add this as a feature with a big warning about this limitation but I'm not sure I like it.
The more advanced approach which I'm planning to implement is much more complex but will achieve the best result. The way it works from a high level is that we'll have a mechanism to find the potentially relevant tags from all the existing tags and pass only those to OpenAI making the request much cheaper. This will take a bit more time to implement though, but it's on my radar.
An option in the settings for forcing AI to use pre-existing tags would allow for more fine-tuned organization
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