semvec/pypi-packages huggingface dataset:
kgdata 7.0.12
Library to process dumps of knowledge graphs (Wikipedia, DBpedia, Wikidata)
License: MIT
Requires Python: <4.0,>=3.10

# kgdata ![PyPI](https://img.shields.io/pypi/v/kgdata) ![Documentation](https://readthedocs.org/projects/kgdata/badge/?version=latest&style=flat)

KGData is a library to process dumps of Wikipedia, Wikidata. What it can do:

- Clean up the dumps to ensure the data is consistent (resolve redirect, remove dangling references)
- Create embedded key-value databases to access entities from the dumps.
- Extract Wikidata ontology.
- Extract Wikipedia tables and convert the hyperlinks to Wikidata entities.
- Create Pyserini indices to search Wikidata’s entities.
- and more

For a full documentation, please see [the website](https://kgdata.readthedocs.io/).

## Installation

From PyPI (using pre-built binaries):

```bash
pip install kgdata[spark]   # omit spark to manually specify its version if your cluster has different version
```
euno-sdk 0.1.0
Euno's CLI library to programmatically interact with Euno instance
License: MIT
Requires Python: >=3.8
Keywords: euno, sdk, cli, api

# Euno SDK

Euno's CLI library to programmatically interact with Euno instance

## Installation

```bash
pip install euno-sdk
```

## Quick Start

### As a Library

```python
from euno import hello_world

# Use the hello_world function
message = hello_world("Euno")
print(message)  # Output: Hello, Euno! Welcome to the Euno SDK!
```

### As a CLI Tool

```bash
# Hello world command
euno hello-world --name Euno

# Show version
euno version

# Show help
euno --help
```

## Development

### Setup Development Environment

```bash
# Clone the repository
git clone https://github.com/euno-ai/euno-sdk.git
cd euno-sdk

# Install development dependencies
pip install -r requirements-dev.txt

# Install the package in development mode
pip install -e .
```

### Running Tests

```bash
pytest
```

### Code Formatting

```bash
black euno tests
```

### Type Checking

```bash
mypy euno
```

## Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for your changes
5. Run the test suite
6. Submit a pull request

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.