Great post indeed! I totally agree that embeddings are underrated. I feel like the "information retrieval/discovery" world is stuck using spears (i.e., term/keyword-based discovery) instead of embracing the modern tools (i.e., semantic-based discovery).
The other day I found myself trying to figure out some common themes across a bunch of comments I was looking at. I felt lazy to go through all of them so I turned my attention to the "Sentence Transformers" lib. I converted each comment into a vector embedding, applied k-means clustering on these embeddings, then gave each cluster to ChatGPT to summarize the corresponding comments. I have to admit, it was fun doing this and saved me lots of time!
The other day I found myself trying to figure out some common themes across a bunch of comments I was looking at. I felt lazy to go through all of them so I turned my attention to the "Sentence Transformers" lib. I converted each comment into a vector embedding, applied k-means clustering on these embeddings, then gave each cluster to ChatGPT to summarize the corresponding comments. I have to admit, it was fun doing this and saved me lots of time!