docs: update future features md

Author: Elan456

docs/future_features.md

@@ -21,13 +21,7 @@ Currently, KNN only supports finding the nearest neighbors based on euclidean di
 By adding a criteria function, we could allow users to define custom criteria for finding neighbors by passing a function that 
 takes in a point and returns a score. The KNN algorithm would then use this score to determine the nearest neighbors.
 
-### 3. KNN in rectangle quadtree
-
-Currently, KNN is only supported in the point quadtree. By adding KNN support to the rectangle quadtree, we could allow users to find the nearest rectangles to a given point. This would be to the nearest edge of the rectangle, adding complexity to the algorithm.
-However, it will allow for really quick collision detection between a point and a set of rectangles as the point can just do
-robust-collision handling with the nearest rectangles.
-
-### 4. Circle support
+### 3. Circle support
 
 Currently, we support points and rectangles in two separate quadtrees.
 For example, in the ball-pit demo, we use a point quadtree, but then query a larger area to account for the radius of the balls.
@@ -39,6 +33,12 @@ A good alternative is to use the rectangle quadtree and insert the minimum bound
 
 Once a feature from above is completed, it will be moved to this section.
 
+### KNN in rectangle quadtree (1.5.0)
+
+Currently, KNN is only supported in the point quadtree. By adding KNN support to the rectangle quadtree, we could allow users to find the nearest rectangles to a given point. This would be to the nearest edge of the rectangle, adding complexity to the algorithm.
+However, it will allow for really quick collision detection between a point and a set of rectangles as the point can just do
+robust-collision handling with the nearest rectangles.
+
 ### Numpy Queries (1.4.0)
 
 To improve performance when querying, we add support to have your result returned in a pre-allocated Numpy array.