First time I ran into embeddings was with word2vec and could not resist showing that, similar to the "king - man + woman ~ queen", it also the case that "yoda -

First time I ran into embeddings was with word2vec and could not resist showing that, similar to the "king - man + woman ~ queen", it also the case that "yoda - good + evil ~ vader". It's also cool that the semantic meaning is the same in vector space, regardless of the language. Embeddings are also super important in retrieval-augmented-generation (RAG), and getting the best "embeddings model" is important to achieve the best RAG performance. At Vectara we recently launched our new Boomerang model that pushes the limit on performance on embedding models, and I hope will spur more innovation and further improvements in this space. https://vectara.com/introducing-boomerang-vectaras-new-and-improved-retrieval-model/ https://vectara.com/introducing-boomerang-vectaras-new-and-i...