Yes, Vespa has a generic Tensor framework that allows you to index multiple vectors for a single field, see https://blog.vespa.ai/semantic-search-with-multi-vector-inde... for details.

field embeddings type tensor<float>(p{}, x[384]) to represent a multi-vector field { "0": [0.1....], "1": [0.2,..] }

yes that is what I meant

generally if you have multiple embeddings for the same document you have two choices:

- create one document for each embedding and make sure non membedding specific attributes are the same across all of this document clones -- vespa makes this more convenient by having child documents

- have a field with multiple documents, i.e. there are multipel vectors in the HNSW-index which point to the same document -- vespa support this, too. It's what I meant.

vespa is currently the only vector search enabled search system which supports both in a convenient way, but then there are so many "vector databases" poping up every month that I might have missed some