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what is going on guys welcome back in this video today weamp;#39;re going to learn how to use large language models locally to create embeddings and to store these embeddings in a vector store in order to be able to do similarity search for example for recommender systems so let us get right into it all right so weamp;#39;re going to learn how to use large language models locally to create embeddings and embeddings are basically just representations in v VOR space of some given data so for example you can have a collection of news articles or titles of news articles and you can embed them into Vector space meaning that one title is then represented as a vector of a certain size all the vectors have the same size and all these vectors can then be stored in a vector database or a vector store and then you can perform similarity search to find the most similar uh articles or article titles given a new article title which can be very useful for recommender systems because you can
