Vector databases are currently all the rage in the tech world, and it isn’t just hype. Vector search has become ever more critical due to artificial intelligence advances which make use of vector embeddings. These vector embeddings are vector representations of word embeddings, sentences, or documents that provide semantic similarity for semantically close inputs by simply looking at a distance metric between the vectors.

The canonical example from word2vec in which the embedding of the word “king” was very near the resulting vector from the vectors of the words “queen”, “man”, and “woman” when arranged in the following formula:

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