docs: update docs to mention generic coordinate types

Author: Elan456

README.md

@@ -33,6 +33,7 @@ Rust-optimized quadtree with a clean Python API
 - Fast KNN and range queries
 - Optional object tracking for id ↔ object mapping
 - Fast [serialization](https://elan456.github.io/fastquadtree/benchmark/#serialization-vs-rebuild) to/from bytes
+- Support for multiple data types (f32, f64, i32, i64) for coordinates
 - [100% test coverage](https://codecov.io/gh/Elan456/fastquadtree) and CI on GitHub Actions
 - Offers a drop-in [pyqtree shim](https://elan456.github.io/fastquadtree/benchmark/#pyqtree-drop-in-shim-performance-gains) that is 6.567x faster while keeping the same API
 

docs/future_features.md

@@ -9,27 +9,19 @@ The features will likely be implemented in the order they are listed below, but
 
 ## 🚧 Planned Features
 
-### 1. Configurable Quadtree Coordinate Type
-
-Currently, the point quadtree only uses f32 for point coordinates, limiting precision in favor of better performance.
-To make the quadtree more flexible, we could allow users to specify the coordinate type (e.g., f64, i32, etc.) when creating a quadtree.
-The f32 will remain the default, but users will be able to specify a different type if needed.
-
-If the type cannot be made truly generic, then only the following types would be supported: f32, f64, i32, i64
-
-### 2. KNN with criteria function
+### 1. KNN with criteria function
 
 Currently, KNN only supports finding the nearest neighbors based on euclidean distance.
 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
+### 2. 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.
@@ -41,6 +33,15 @@ 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.
 
-### Quadtree serialization
+
+### Configurable Quadtree Coordinate Type (1.3.0)
+
+Currently, the point quadtree only uses f32 for point coordinates, limiting precision in favor of better performance.
+To make the quadtree more flexible, we could allow users to specify the coordinate type (e.g., f64, i32, etc.) when creating a quadtree.
+The f32 will remain the default, but users will be able to specify a different type if needed.
+
+If the type cannot be made truly generic, then only the following types would be supported: f32, f64, i32, i64
+
+### Quadtree serialization (1.2.0)
 
 By serializing the quadtree, we can save its state to a file and load it later. This will allow us to persist the quadtree structure and data across sessions. For example, you could pre build a quadtree with all the walls in your video game level, serialize it to a file, and then load it when the game starts. This will heavily reduce the game load time since you won't have to rebuild the quadtree from scratch every time.

docs/index.md

@@ -42,6 +42,7 @@
 - Fast KNN and range queries
 - Optional object tracking for id ↔ object mapping
 - Fast [serialization](benchmark.md#serialization-vs-rebuild) to/from bytes
+- Support for multiple data types (f32, f64, i32, i64) for coordinates
 - [100% test coverage](https://codecov.io/gh/Elan456/fastquadtree) and CI on GitHub Actions
 
 ## Examples