From cdae5b79a06616d6864b71bef9634de219758e57 Mon Sep 17 00:00:00 2001
From: Radko Stanev <49035696+radkostanev@users.noreply.github.com>
Date: Sun, 13 Apr 2025 11:39:45 +0300
Subject: [PATCH] Remove duplication from what-is-a-vector-database.mdx

---
 .../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 71fae98b44cb4e..8fef82db8d9b68 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
@@ -13,10 +13,6 @@ Without a vector database, you would need to train your model (or models) or re-
 
 A vector database determines what other data (represented as vectors) is near your input query. This allows you to build different use-cases on top of a vector database, including:
 
-- Semantic search, used to return results similar to the input of the query.
-- Classification, used to return the grouping (or groupings) closest to the input query.
-- Recommendation engines, used to return content similar to the input based on different criteria (for example previous product sales, or user history).
-- Anomaly detection, used to identify whether specific data points are similar to existing data, or different.
 - Semantic search, used to return results similar to the input of the query.
 - Classification, used to return the grouping (or groupings) closest to the input query.
 - Recommendation engines, used to return content similar to the input based on different criteria (for example previous product sales, or user history).