From 4d32acd342b23bf7ef070a940b8c9ce6d90c2eb1 Mon Sep 17 00:00:00 2001 From: Derrick Mwiti Date: Mon, 9 Jun 2025 13:25:54 +0300 Subject: [PATCH] update image location --- .../advanced-tutorials/using-multivector-representations.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/documentation/advanced-tutorials/using-multivector-representations.md b/qdrant-landing/content/documentation/advanced-tutorials/using-multivector-representations.md index f346aebf3..415efc993 100644 --- a/qdrant-landing/content/documentation/advanced-tutorials/using-multivector-representations.md +++ b/qdrant-landing/content/documentation/advanced-tutorials/using-multivector-representations.md @@ -14,7 +14,7 @@ In most vector engines, each document is represented by a single vector - an app 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. -![Multivector Representations](documentation/advanced-tutorials/multivector.png) +![Multivector Representations](/documentation/advanced-tutorials/multivector.png) As you will see later in the tutorial, Qdrant supports multivectors and thus late interaction models natively.