Intertopic Distance Map keeps changing every time I rerun it

Hello there,

So I am pretty new to this, but I am really interested in using this to explore my corpus. I am not sure if this is inherent in the code itself, but whenever I try to rerun it, it keeps loading a different form of intertopic distance map. So this means that I cannot replicate it which is not ideal. I have attached below the codes that I used. Thank you!

`
df_clean = df.dropna(subset=['Policy_Content'])
umap = UMAP(n_neighbors=15,
n_components=5,
min_dist=0.0,
metric='cosine',
low_memory=False,
random_state=123)
vectorizer_model = CountVectorizer(stop_words="english", min_df=2, ngram_range=(1, 2))

topic_model = BERTopic(umap_model=umap,vectorizer_model=vectorizer_model, verbose=True)
topics, probs = topic_model.fit_transform(df_clean['Policy_Content'])
#227 topics in total
topic_model.reduce_topics(df_clean['Policy_Content'], nr_topics=48)

topic_model.visualize_topics()
`

Warm regards,
Yanith

Start with the provided Python snippet and the UMAP, BERTopic.fit_transform, reduce_topics, and visualize_topics calls. Rerun the same corpus and configuration while checking how each stage affects the intertopic distance map. Done means identifying the source of the differing outputs and establishing whether the result can be reproduced with this setup.