![]() ![]() Search engines need to be fast and searching over 100 dimensions across a data set of over 200k records is very resource intensive. Given the high number of dimensions (100) in our vectors this is where things could start to fall down with our approach. We can also use the techniques outlined above to create a vector for a search query from a user.īut how do we return relevant results based on this search query? We need to be able to find the closest vectors to our search vector. We have now have a list of vectors for each document in our data set. Create a super-fast search index with NMSLIB The output from this will give us a single vector per document in our search engine. Here we are building a fastText model:Ĭonverting our word vectors into a document vector weighted using BM25 ![]() In addition to the above they are super simple to implement using the Gensim library.
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