Update cosine-similarity.md

docs/concepts/cosine-similarity.md

@@ -9,20 +9,20 @@ layout: default
 
 Cosine similarity is a measure of the degree of similarity between two vectors in
 a multidimensional space. It is commonly used in artificial intelligence and natural
-language processing to compare [embeddings](/concepts/embedding),
+language processing to compare [embeddings](embedding),
 which are numerical representations of
 words or other objects.
 
 The cosine similarity between two vectors is calculated by taking the
-[dot product](/concepts/dot-product) of the two vectors and dividing it by the product
+[dot product](dot-product) of the two vectors and dividing it by the product
 of their magnitudes. This results in a value between -1 and 1, where 1 indicates
 that the two vectors are identical, 0 indicates that they are orthogonal
 (i.e., have no correlation), and -1 indicates that they are opposite.
 
 Cosine similarity is particularly useful when working with high-dimensional data
 such as word embeddings because it takes into account both the magnitude and direction
 of each vector. This makes it more robust than other measures like
-[Euclidean distance](/concepts/euclidean-distance), which only considers the magnitude.
+[Euclidean distance](euclidean-distance), which only considers the magnitude.
 
 One common use case for cosine similarity is to find similar words based on their
 embeddings. For example, given an embedding for "cat", we can use cosine similarity