using representation model takes much longer

Nobody has claimed this yet.

Hello i am running BERTopic on a mabook pro m1 with the following parameters using precomputed embeddings with sentence transformer

```
vectorizer_model = CountVectorizer(stop_words="english")
ctfidf_model = ClassTfidfTransformer(reduce_frequent_words=True)
representation_model = OpenAI(openai_client, model="gpt-3.5-turbo", delay_in_seconds=10, chat=True)

BERTopic(
vectorizer_model = vectorizer_model,
ctfidf_model = ctfidf_model,
nr_topics = 'auto',
min_topic_size = max(int(len(docs)/800),10),
representation_model = representation_model )
```

i noticed a big difference in the fitting time of the model using the representation model, 5 min without, 35 min with

is there a particular reason for that? since it is something that should be run at the end of the entire process of topic modeling it should not take 30 min to retrieve keywords and docs and send a prompt to chat gpt, maybe there is something happening that i am not aware of, thanks in advance

is there maybe a possibility to add the representation layer after fitting the model?

Start by reproducing the reported BERTopic fitting times with and without the shown OpenAI representation_model settings. Trace when representation processing occurs and assess whether it can be applied after fitting; done means providing a supported post-fit workflow or clearly documenting why the extra time is expected.

Written by the indexing model from the issue text.