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As a suggestion for designing really capable, scalable systems for document similarity I'd suggest reading Chapter 3 of Mining Massive Datasets, which is freely available online. One approach presented there is to 'shingle' datasets by vectorizing word counts into sets, then hashing those word counts and comparing families of hashes results with Jaccard similarity to get a score between all documents. This can work on petabytes of files with high precision if done right. Explicit details with good diagrams can be read off Stanford's CS246 Slides on Locality Sensitive Hashing. Simpler approaches like word frequency counting are described in the book as well.