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* At least any open-source model, since you need access to its internals.
You Can Adapt Dense Embedding Models for Late Interaction Qdrant 1.10 introduced support for multi-vector representations, with late interaction being a prominent example of this model. In essence, both documents and queries are represented by multiple vectors, and identifying the most relevant documents involves calculating a score based on the similarity between the corresponding query and document embeddings.