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Hello, author here. I wrote this guide up after spending a long time trying to wrap my head around LangChain and the associated ecosystem, and hope others find

• sixhobbits 3y ago
  hey I have wanted to write a post like this for ages but of course I never got around to it, and now I can just link people to this one instead! My only suggestions are: - Coining (? at least I think you did) the new term "Embedding machine" I think loses some clarity versus using "vectorizer", or other existing terminology. - I know that researchers were surprised by how well word embeddings work, but I think you could have built a bit more intuition here instead of just saying "black box" (but maybe article would have gotten too long) - maybe using some of the intuitions from word2vec, even though that is dated now, the idea that 'similar tokens appear in similar contexts' is useful I think.
  • czue 3y ago
    Thanks for the input and will very happily accept you sending links to this post. :) My use of "embedding machine" is just revealing my own ignorance. Never heard the term "vectorizer" before, but will switch it (or find a better word). I'll also have to do some reading to educate myself on word2vec. That's this? https://en.wikipedia.org/wiki/Word2vec https://en.wikipedia.org/wiki/Word2vec
    • sixhobbits 3y ago
      yes, the most-quoted 'breakthrough' was figuring out analogies like this https://www.technologyreview.com/2015/09/17/166211/king-man-woman-queen-the-marvelous-mathematics-of-computational-linguistics/ https://www.technologyreview.com/2015/09/17/166211/king-man-...
    • sharemywin 3y ago
      you might want to check these out too: https://www.sbert.net/docs/pretrained_models.html#sentence-embedding-models/ https://www.sbert.net/docs/pretrained_models.html#sentence-e... that way you don't need to pay openai for the embeddings