Each vector is an array of n floats that represent a location of a thing in an n-dimensional space. The idea of learning an embedding is that you have some learning process that will put items that are similar into similar parts of that vector space.
The vectors don’t necessarily need to represent words and the model that produces them doesn’t necessarily to be a language model.
For example, embeddings are widely used to generate recommendations. Say you have a dataset of users clicking on products on a website. You could assume that products that get clicked in the same session are probably similar and use that dataset to learn an embedding for products. This would give you vector representing each product. When you want to generate recommendations for a product, you take the vector for that product and then search through the set of all product vectors to find those that are closest to it in the vector space.
I’ve worked on e-commerce recs. Typically you would represent each product with a vector. Then finding similar products becomes a nearest-neighbour search over these vectors. Depending on your use-case it’s feasible now to do this search in the db using pgvector, or using something like solr/elastic which both support vector search in recent releases. You could also use something like faiss or one of the many nearest-neighbour libraries or dedicated vector search engines. (Since you are working with Elixir, you might find ExFaiss interesting [1][2][3]).
But I would say that for recommendations, searching the vectors is the easy part. The main work in getting good recommendations is generating a good set of product vectors in the first place. The quality of the recommendations is directly related how you generate the vectors. You could use one of the many open-source language models to generate vectors, but typically that approach isn’t very good for product recommendations. It will just give you items that are textually similar, and this usually doesn’t give good product recommendations.
To get good product recommendations you’d probably want to build a custom embedding that captures some notion of product similarity during training using some signals you get from user behaviour. E.g. things like products clicked in the same session, or added to cart at the same time, gives a signal on product similarity that you can use to train a product embedding for recommendations.
This is a bit more involved, but the main work is in generating the training data. Once you have that you can use open source tools such as fasttext [4] to learn the embedding and output the product vectors. (Or if you want to void training your own embedding, I’d guess that there are services that will take your interaction data and generate product vectors from them, but I’m not familiar with any).
Each vector is an array of n floats that represent a location of a thing in an n-dimensional space. The idea of learning an embedding is that you have some learning process that will put items that are similar into similar parts of that vector space.
The vectors don’t necessarily need to represent words and the model that produces them doesn’t necessarily to be a language model.
For example, embeddings are widely used to generate recommendations. Say you have a dataset of users clicking on products on a website. You could assume that products that get clicked in the same session are probably similar and use that dataset to learn an embedding for products. This would give you vector representing each product. When you want to generate recommendations for a product, you take the vector for that product and then search through the set of all product vectors to find those that are closest to it in the vector space.