not because they’re sufficiently advanced technology indistinguishable from magic, but the opposite.
Unlike LLMs, working with embeddings feels like regular deterministic code.
<h3>Creating embeddings</h3>
I was hoping for a bit more than:
They’re a bit of a black box
Next, we chose an embedding model. OpenAI’s embedding models will probably work just fine.
Same here. I was saving the article for when I have a few hours to really dive into it, build upon it, learn from seeing and doing. Imagine my disappointment when I had the evening cleared, started reading, and discover all they're showing is how to concatenate a string, download someone else's black box model which outputs the similarity between the user's query and the concatenated info about each object, and then write queries on the output
It's good to know you can do this performantly on your own system, but if the article had started out with "look, this model can output similarity between two texts and we can make a search engine with that", that'd be much more up front about what to expect to learn from it
Edit: another comment mentioned you can't even run it yourself, you need to ask ClosedAI for every search query a user does on your website. WTF is this article, at that point you might as well pipe the query into general-purpose chatgpt which everyone already knows and let that sort it out
I agree. The article was useful insofar as it detailed the steps they took to solve their problem clearly, and it's easy to see that many common problems are similar and could therefore be solved similarly, but I went in expecting more insight. How are the strings turned into arrays of numbers? Why does turning them into numbers that way lead to these nice properties?
