Fix the inconsistencies in the documentation (#16)

Author: habedi

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vq"
-version = "0.2.0-alpha.3"
+version = "0.2.0"
 description = "A vector quantization library for Rust"
 repository = "https://github.com/CogitatorTech/vq"
 license = "MIT OR Apache-2.0"

README.md

@@ -167,8 +167,8 @@ make eval-all
 
 > [!NOTE]
 > To run the benchmarks, you need to have GNU Make installed.
-> The `make eval-all` command will run each quantizer on a set of high-dimensional synthetic data and report runtime (ms) and reconstruction
-> error (RMSE).
+> The `make eval-all` command will run each quantizer on a set of high-dimensional synthetic data and report runtime (in millisecond)
+> and reconstruction error (in mean squared error).
 > See [src/bin/common.rs](src/bin/common.rs) for parameters used in the benchmarks like size of the training data, dimensions, etc.
 
 ---

docs/index.md

@@ -1,6 +1,6 @@
 # Vq
 
-Vq (**v**ector **q**uantizer) is a vector quantization library for Rust.
+Vq (**v**ector **q**uantizer) is a vector quantization library for Rust 🦀.
 It provides efficient implementations of popular quantization algorithms for compressing high-dimensional vectors.
 
 ## Features
@@ -13,12 +13,12 @@ It provides efficient implementations of popular quantization algorithms for com
 
 ## Supported Algorithms
 
-| Algorithm              | Training      | Quantization  | Compression |
-|------------------------|---------------|---------------|-------------|
-| Binary (BQ)            | $O(1)$        | $O(nd)$       | 75%         |
-| Scalar (SQ)            | $O(1)$        | $O(nd)$       | 75%         |
-| Product (PQ)           | $O(nkd)$      | $O(nd)$       | 50%         |
-| Tree-Structured (TSVQ) | $O(n \log k)$ | $O(d \log k)$ | 50%         |
+| Algorithm              | Training Complexity | Quantization Complexity | Minimum Storage Reduction |
+|------------------------|---------------------|-------------------------|---------------------------|
+| Binary (BQ)            | $O(1)$              | $O(nd)$                 | 75%                       |
+| Scalar (SQ)            | $O(1)$              | $O(nd)$                 | 75%                       |
+| Product (PQ)           | $O(nkd)$            | $O(nd)$                 | 50%                       |
+| Tree-Structured (TSVQ) | $O(n \log k)$       | $O(d \log k)$           | 50%                       |
 
 Where $n$ is number of vectors, $d$ is the number of dimensions of a vector, and $k$ is the number of centroids used in clustering (for PQ and TSVQ).
 
@@ -41,7 +41,7 @@ fn main() -> vq::VqResult<()> {
 
 ## Python Bindings
 
-Python bindings are available via [PyVq](https://pypi.org/project/pyvq/):
+Python 🐍 bindings are available via [PyVq](https://pypi.org/project/pyvq/):
 
 ```bash
 pip install pyvq

pyvq/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "pyvq"
-version = "0.1.0-alpha.3"
+version = "0.1.0"
 edition = "2024"
 publish = false
 

pyvq/README.md

@@ -36,6 +36,9 @@ dist = pyvq.Distance.euclidean()
 a = np.array([1.0, 2.0, 3.0], dtype=np.float32)
 b = np.array([4.0, 5.0, 6.0], dtype=np.float32)
 print(f"Distance: {dist.compute(a, b)}")
+
+# Check the SIMD backend in use
+print(f"SIMD Backend: {pyvq.get_simd_backend()}")
 ```
 
 ### Documentation

pyvq/docs/index.md

@@ -1,14 +1,14 @@
 # PyVq
 
-PyVq provides Python bindings for the [Vq](https://github.com/CogitatorTech/vq) vector quantization library.
+PyVq provides Python 🐍 bindings for the [Vq](https://github.com/CogitatorTech/vq) vector quantization library.
 
 ## Features
 
-- High-performance Rust implementation with Python bindings
+- High-performance Rust 🦀 implementation with Python bindings
 - NumPy array support for input and output
-- All quantization algorithms: BinaryQuantizer, ScalarQuantizer, ProductQuantizer, TSVQ
-- SIMD-accelerated distance computations
-- Simple, Pythonic API
+- All quantization algorithms in Vq: BinaryQuantizer, ScalarQuantizer, ProductQuantizer, TSVQ
+- Fast SIMD-accelerated distance computations
+- A simple, Pythonic API
 
 ## Quick Example
 
@@ -39,11 +39,10 @@ print(f"Euclidean distance: {result}")
 ## Installation
 
 ```bash
+# PyVq works with Python 3.10 or later
 pip install pyvq
 ```
 
-Requires Python 3.10 or later.
-
 ## Documentation
 
 - [Getting Started](getting-started.md) - Installation and first steps
@@ -55,4 +54,4 @@ Requires Python 3.10 or later.
 For the Rust library documentation, see [docs.rs/vq](https://docs.rs/vq) or the [main documentation](https://cogitatortech.github.io/vq/).
 
 !!! note "Early Development"
-    PyVq is in early development. Please report bugs on [GitHub Issues](https://github.com/CogitatorTech/vq/issues).
+    PyVq is in early development. Please report bugs on project's [issues page](https://github.com/CogitatorTech/vq/issues) on GitHub.

pyvq/pyproject.toml

@@ -16,7 +16,7 @@ license = { text = "MIT" }
 readme = "README.md"
 dynamic = ['version']
 classifiers = [
-    "Development Status :: 3 - Alpha",
+    "Development Status :: 4 - Beta",
     "Intended Audience :: Developers",
     "Intended Audience :: Science/Research",
     "License :: OSI Approved :: MIT License",

src/bin/common.rs

@@ -8,7 +8,8 @@ use vq::Vector;
 
 pub const SEED: u64 = 66;
 pub const NUM_SAMPLES: [usize; 6] = [1_000, 5_000, 10_000, 50_000, 100_000, 1_000_000];
-pub const DIM: usize = 128;
+// Some typical dimension for embedding vectors are 128, 256, 384, 768, and 1536
+pub const DIM: usize = 384;
 pub const M: usize = 16;
 pub const K: usize = 256;
 pub const MAX_ITERS: usize = 10;