Metadata-Version: 2.4
Name: genestore
Version: 0.2.0
Classifier: Programming Language :: Rust
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Database :: Front-Ends
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Rust
Classifier: Operating System :: OS Independent
License-File: LICENSE
Summary: Python bindings for ArrowSpace (Rust) providing graph-based similarity search, signal graphs, and spectral methods for vector data.
Keywords: vector-search,spectral-analysis,graph-signal,similarity-search,machine-learning
Home-Page: https://github.com/tuned-org-uk/pygenestore
License-Expression: MIT OR Apache-2.0
Requires-Python: >=3.12
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Homepage, https://github.com/tuned-org-uk/pygenestore
Project-URL: Repository, https://github.com/tuned-org-uk/pygenestore.git

# pygenestore

Store your `numpy` arrays at scale using the Lance format.

## Usage

```python
import numpy as np
import genestore

# Create a storage builder and configure it
builder = genestore.store_array("./lance_data")
builder.with_max_rows_per_file(500000)
builder.with_compression("zstd")

# Build the storage instance
storage = builder.build()

# Create a numpy array (dense matrix)
data = np.random.randn(1000, 128).astype(np.float64)

# Store the array
path = storage.store(data, "my_dataset")
print(f"Stored at: {path}")

# Load the array back
loaded_data = storage.load(path)
print(f"Loaded shape: {loaded_data.shape}")
```

## Tests


```bash
pip install -r requirements-dev.txt
pytest tests/
```

