Spectral hashing is a learning-to-hash (L2H) technique for partitioning a collection of vectors. It involves finding low-dimensional projections related to the principal components of the vector distribution.
To the best of my knowledge, this strategy is not used in any mainstream vector database or library. Rather, as the main benefit of this strategy is a reduction in the dimensionality of the task, quantization techniques are preferred.