From d8e598ba9de3f08269f5dc85ff74a1977d54c21d Mon Sep 17 00:00:00 2001
From: Vikram Negi <82281115+lostvikx@users.noreply.github.com>
Date: Thu, 17 Apr 2025 00:12:24 +0530
Subject: [PATCH] Update what-is-a-vector-database.mdx

The bullet points were duplicate.
---
 .../docs/vectorize/reference/what-is-a-vector-database.mdx    | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/src/content/docs/vectorize/reference/what-is-a-vector-database.mdx b/src/content/docs/vectorize/reference/what-is-a-vector-database.mdx
index 8fef82db8d9b68..2cafbd7aee780d 100644
--- a/src/content/docs/vectorize/reference/what-is-a-vector-database.mdx
+++ b/src/content/docs/vectorize/reference/what-is-a-vector-database.mdx
@@ -88,10 +88,6 @@ Refer to the [dimensions](/vectorize/best-practices/create-indexes/#dimensions)
 
 The distance metric is an index used for vector search. It defines how it determines how close your query vector is to other vectors within the index.
 
-- Distance metrics determine how the vector search engine assesses similarity between vectors.
-- Cosine, Euclidean (L2), and Dot Product are the most commonly used distance metrics in vector search.
-- The machine learning model and type of embedding you use will determine which distance metric is best suited for your use-case.
-- Different metrics determine different scoring characteristics. For example, the `cosine` distance metric is well suited to text, sentence similarity and/or document search use-cases. `euclidean` can be better suited for image or speech recognition use-cases.
 - Distance metrics determine how the vector search engine assesses similarity between vectors.
 - Cosine, Euclidean (L2), and Dot Product are the most commonly used distance metrics in vector search.
 - The machine learning model and type of embedding you use will determine which distance metric is best suited for your use-case.