[P] Evaluating several topic modeling implementations. What's the current best practice? BERTopic? OpenAI Ada-002?

I have a set of ~100 topic categories, and I want to determine which are semantically close to a text input.

I've found several implementations, but I know some (LDA) are already obsolete. OpenAI's text-embedding-ada-002 model just came out so I'm wondering if that's the best option now.

Other topic modeling implementations:

Multi-Class Text Classification with Doc2Vec & Logistic Regression

Build taxonomy-based contextual targeting using AWS Media Intelligence and Hugging Face BERT

Topic Modeling with BERTopic