Update what-is-a-vector-database.mdx

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.