[Vectorize] Fix typo

Author: netgusto

src/content/docs/vectorize/best-practices/create-indexes.mdx

@@ -105,6 +105,6 @@ Distance metrics are functions that determine how close vectors are from each ot
 
 Determining the similarity between vectors can be subjective based on how the machine-learning model that represents features in the resulting vector embeddings. For example, a score of `0.8511` when using a `cosine` metric means that two vectors are close in distance, but whether data they represent is _similar_ is a function of how well the model is able to represent the original content.
 
-When querying vectors, you can specify Vectorize to either use high-precision scoring, increasing the precision of the query matches scores as well as the accuracy of the query results, or use approximate scoring for faster response times. Using approximate scoring, returned scores will be an approximation of the real distance/similarity between your query and the returned vector. See [Control over scoring precision and query accuracy](/vectorize/best-practices/query-vectors/#control-over-scoring-precision-and-query-accuracy)
+When querying vectors, you can specify Vectorize to either use high-precision scoring, increasing the precision of the query matches scores as well as the accuracy of the query results, or use approximate scoring for faster response times. Using approximate scoring, returned scores will be an approximation of the real distance/similarity between your query and the returned vectors. See [Control over scoring precision and query accuracy](/vectorize/best-practices/query-vectors/#control-over-scoring-precision-and-query-accuracy)
 
 Distance metrics cannot be changed after index creation, and that each metric has a different scoring function.

src/content/docs/vectorize/best-practices/query-vectors.mdx

@@ -74,7 +74,7 @@ Refer to [Vectorize API](/vectorize/reference/client-api/) for additional exampl
 ## Control over scoring precision and query accuracy
 
 When querying vectors, you can specify to either use high-precision scoring, increasing the precision of the query matches scores as well as the accuracy of the query results, or use approximate scoring for faster response times.
-Using approximate scoring, returned scores will be an approximation of the real distance/similarity between your query and the returned vector.
+Using approximate scoring, returned scores will be an approximation of the real distance/similarity between your query and the returned vectors.
 
 High-precision scoring is enabled by setting `returnValues: true` on your query; this tells Vectorize to fetch and use the original vector values for your matches, which enables the computation of exact scores of matches, increasing the accuracy of the results.