tensorstore VS postgres-word2vec

shhh...don't tell them about Tensorflow, or tf.Tensor, or theano.tensor, or paddle.to_tensor or torch.Tensor or torch.Tensor or mindspore.Tensor or tensorstore or ... But in all seriousness your pitchforks might be a little late to the party here. Rightly or wrongly (large) segments of the machine learning community have adopted terminology to describe things (for your sake, I hope you have not seen what they did with the term 'deconvolution'). This is not a new phenomena either and has been happening for at least a decade and almost certainly longer. However, there are other libraries that use the array terminology np.array, mx.nd.array, chainer, jax.numpy.array so it is definitely not unanimous. Whether you think this is an egregious mis-representation of the nomenclature or not is irrelevant now as it has a pretty established use within the community. Language is an evolving untamed beast - don't get angry, get building!

There are tons of other ways to store vector data, one was just recently released - https://github.com/google/tensorstore

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Today we are introducing TensorStore, an open-source C++ and Python software library designed for storage and manipulation of n-dimensional data that:

I've recently stumbled upon smaller projects, like FREDDY (https://github.com/guenthermi/postgres-word2vec), a Postgres extension that looks interesting. The ability to write ad-hoc similarity queries in SQL seems like it might be valuable in some circumstances. I'm not sure about performance or storage efficacy.

The syntactic similarity is not enough for you? Word2vec could be an option to compare the semantic similarity of words. Luckily there already is a postgres-word2vec extension for this.