docs:First complete version.

Author: antononcube

README-work.md

@@ -0,0 +1,62 @@
+# Math::DistanceFunctions::Native
+
+[![Actions Status](https://github.com/antononcube/Raku-LibAccelerate-DistanceFunctions/actions/workflows/linux.yml/badge.svg)](https://github.com/antononcube/Raku-LibAccelerate-DistanceFunctions/actions)
+[![Actions Status](https://github.com/antononcube/Raku-LibAccelerate-DistanceFunctions/actions/workflows/macos.yml/badge.svg)](https://github.com/antononcube/Raku-LibAccelerate-DistanceFunctions/actions)
+
+[![License: Artistic-2.0](https://img.shields.io/badge/License-Artistic%202.0-0298c3.svg)](https://opensource.org/licenses/Artistic-2.0)
+
+Raku package with distance functions implemented in C.
+Apple's Accelerate library is used if available.
+
+The primary motivation for making this library is to have fast sorting and nearest neighbors computations 
+over collections of LLM-embedding vectors.
+
+------
+
+## Usage examples
+
+
+### Regular vectors
+
+Make a large (largish) collection of large vectors and find Euclidean distances over them:
+
+```perl6
+use Math::DistanceFunctions::Native;
+
+my @vecs = (^1000).map({ (^1000).map({1.rand}).cache.Array }).Array;
+my @searchVector = (^1000).map({1.rand});
+
+my $start = now;
+my @dists = @vecs.map({ euclidean-distance($_, @searchVector)});
+my $tend = now;
+say "Total time of computing {@vecs.elems} distances: {round($tend - $start, 10 ** -6)} s";
+say "Average time of a single distance computation: {($tend - $start) / @vecs.elems} s";
+```
+
+### `CArray` vectors
+
+Use `CArray` vectors instead:
+
+```perl6
+use NativeCall;
+my @cvecs = @vecs.map({ CArray[num64].new($_) });
+my $cSearchVector = CArray[num64].new(@searchVector);
+
+$start = now;
+my @cdists = @cvecs.map({ euclidean-distance($_, $cSearchVector)});
+$tend = now;
+say "Total time of computing {@cvecs.elems} distances: {round($tend - $start, 10 ** -6)} s";
+say "Average time of a single distance computation: {($tend - $start) / @cvecs.elems} s";
+```
+
+I.e., we get ≈ 200 times speed-up using `CArray` vectors and the functions of this package. 
+
+### Edit distance
+
+The loading of this package automatically loads the (C-implemented) function `edit-distance` of
+["Math::DistanceFunctions::Edit"](https://github.com/antononcube/Raku-Math-DistanceFunctions-Edit).
+Here is an example usage:
+
+```perl6
+edit-distance('racoon', 'raccoon')
+```

README.md

@@ -5,5 +5,69 @@
 
 [![License: Artistic-2.0](https://img.shields.io/badge/License-Artistic%202.0-0298c3.svg)](https://opensource.org/licenses/Artistic-2.0)
 
-Raku package with distance functions implemented using Apple's Accelerate library.
-Generic C implementations are also provided.
+Raku package with distance functions implemented in C.
+Apple's Accelerate library is used if available.
+
+The primary motivation for making this library is to have fast sorting and nearest neighbors computations 
+over collections of LLM-embedding vectors.
+
+------
+
+## Usage examples
+
+
+### Regular vectors
+
+Make a large (largish) collection of large vectors and find Euclidean distances over them:
+
+```perl6
+use Math::DistanceFunctions::Native;
+
+my @vecs = (^1000).map({ (^1000).map({1.rand}).cache.Array }).Array;
+my @searchVector = (^1000).map({1.rand});
+
+my $start = now;
+my @dists = @vecs.map({ euclidean-distance($_, @searchVector)});
+my $tend = now;
+say "Total time of computing {@vecs.elems} distances: {round($tend - $start, 10 ** -6)} s";
+say "Average time of a single distance computation: {($tend - $start) / @vecs.elems} s";
+```
+```
+# Total time of computing 1000 distances: 0.63326 s
+# Average time of a single distance computation: 0.0006332598499999999 s
+```
+
+### `CArray` vectors
+
+Use `CArray` vectors instead:
+
+```perl6
+use NativeCall;
+my @cvecs = @vecs.map({ CArray[num64].new($_) });
+my $cSearchVector = CArray[num64].new(@searchVector);
+
+$start = now;
+my @cdists = @cvecs.map({ euclidean-distance($_, $cSearchVector)});
+$tend = now;
+say "Total time of computing {@cvecs.elems} distances: {round($tend - $start, 10 ** -6)} s";
+say "Average time of a single distance computation: {($tend - $start) / @cvecs.elems} s";
+```
+```
+# Total time of computing 1000 distances: 0.002994 s
+# Average time of a single distance computation: 2.994124e-06 s
+```
+
+I.e., we get ≈ 200 times speed-up using `CArray` vectors and the functions of this package. 
+
+### Edit distance
+
+The loading of this package automatically loads the (C-implemented) function `edit-distance` of
+["Math::DistanceFunctions::Edit"](https://github.com/antononcube/Raku-Math-DistanceFunctions-Edit).
+Here is an example usage:
+
+```perl6
+edit-distance('racoon', 'raccoon')
+```
+```
+# 1
+```