Add BERTopic.

LLMTopicDetection_BERTopic/docs/getting_started/search/search.md

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+After having created a BERTopic model, you might end up with over a hundred topics. Searching through those
+can be quite cumbersome especially if you are searching for a specific topic. Fortunately, BERTopic allows you
+to search for topics using search terms. First, let's create and train a BERTopic model:
+
+
+```python
+from bertopic import BERTopic
+from sklearn.datasets import fetch_20newsgroups
+
+# Create topics
+docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']
+topic_model = BERTopic()
+topics, probs = topic_model.fit_transform(docs)
+```
+
+After having trained our model, we can use `find_topics` to search for topics that are similar
+to an input search_term. Here, we are going to be searching for topics that closely relate the
+search term "motor". Then, we extract the most similar topic and check the results:
+
+```python
+>>> similar_topics, similarity = topic_model.find_topics("motor", top_n=5)
+>>> topic_model.get_topic(similar_topics[0])
+[('bike', 0.02275997701645559),
+ ('motorcycle', 0.011391202866080292),
+ ('bikes', 0.00981187573649205),
+ ('dod', 0.009614623748226669),
+ ('honda', 0.008247663662558535),
+ ('ride', 0.0064683227888861945),
+ ('harley', 0.006355502638631013),
+ ('riding', 0.005766601561614182),
+ ('motorcycles', 0.005596372493714447),
+ ('advice', 0.005534544418830091)]
+```
+
+It definitely seems that a topic was found that closely matches "motor". The topic seems to be motorcycle
+related and therefore matches our "motor" input. You can use the `similarity` variable to see how similar
+the extracted topics are to the search term.
+
+!!! note
+    You can only use this method if an embedding model was supplied to BERTopic using `embedding_model`.