Embedding.md

@@ -38,10 +38,12 @@ They can be stored as normal arrays and reused as needed
 
 Simmilarity function is based on *Eucilidean distance* between all points in vector  
 *Eucliean distance is limited case of Minkowski distance with order of 2*  
-*[Minkowski distance](https://en.wikipedia.org/wiki/Minkowski_distance) is a nth root of sum of nth powers of distances between each point in (each value in 192-member array)*  
+*[Minkowski distance](https://en.wikipedia.org/wiki/Minkowski_distance) is a nth root of sum of nth powers of distances between each point (each value in 192-member array)*  
 
 Changing `order` can make simmilarity matching more or less sensitive:
 
 ```js
   const distance = ((firstEmbedding.map((val, i) => (val - secondEmbedding[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));
 ```
+
+*Once embedding values are calculated and stored, if you want to use stored embedding values without requiring `Human` library you can use above formula to calculate simmilarity on the fly.*