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qdrant-landing/content/documentation/advanced-tutorials/using-multivector-representations.md

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 In most vector engines, each document is represented by a single vector - an approach that works well for short texts but often struggles with longer documents. Single vector representations perform pooling of the token-level embeddings, which obviously leads to losing some information.
 
 Multivector representations offer a more fine-grained alternative where a single document is represented using multiple vectors, often at the token or phrase level. This enables more precise matching between specific query terms and relevant parts of the document. Matching is especially effective in Late Interaction models like [ColBERT](https://qdrant.tech/documentation/fastembed/fastembed-colbert/), which retain token-level embeddings and perform interaction during query time leading to relevance scoring.
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 ![Multivector Representations](images/image.png)
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 As you will see later in the tutorial, Qdrant supports multivectors and thus late interaction models natively. 
 
 ## Why Token-level Vectors are Useful