From bae79ea4169926fe1633f72d911ac591fe07e288 Mon Sep 17 00:00:00 2001 From: Broda Noel Date: Thu, 14 Aug 2025 05:58:06 +0200 Subject: [PATCH] doc: Update sparse-vectors.md --- qdrant-landing/content/articles/sparse-vectors.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/articles/sparse-vectors.md b/qdrant-landing/content/articles/sparse-vectors.md index a1143048f..11476377a 100644 --- a/qdrant-landing/content/articles/sparse-vectors.md +++ b/qdrant-landing/content/articles/sparse-vectors.md @@ -26,7 +26,7 @@ Sparse vectors are like the Marie Kondo of data—keeping only what sparks joy ( Consider a simplified example of 2 documents, each with 200 words. A dense vector would have several hundred non-zero values, whereas a sparse vector could have, much fewer, say only 20 non-zero values. -In this example: We assume it selects only 2 words or tokens from each document. The rest of the values are zero. This is why it's called a sparse vector. +In this example: We assume it selects only 2 words or tokens from each document. ```python dense = [0.2, 0.3, 0.5, 0.7, ...] # several hundred floats