Welcome to WDBX-Py, a versatile vector database system crafted specifically for AI applications. With its extensible plugin architecture, WDBX allows you to adapt to various use cases, from simple to complex scenarios. It also supports distributed servers, making it a robust solution for modern AI needs.

• Features
• Installation
• Usage
• Plugins
• Contributing
• License
• Releases
• Contact

• Extensible Architecture: Easily add new functionalities with plugins.
• Error Optimization: The codebase is designed to minimize errors and maximize performance.
• Distributed Support: Scale your applications across multiple servers.
• Security: Built with secure practices in mind.
• Sharded Database: Efficiently manage large datasets with sharding capabilities.
• Performance: Optimized for high-speed data retrieval and processing.

To get started with WDBX-Py, follow these simple steps:

1. Clone the repository:

   git clone https://github.com/Maric-ui/wdbx-py.git

2. Navigate to the project directory:

   cd wdbx-py

3. Install the required packages:

   pip install -r requirements.txt

4. Run the application:

   python main.py

Using WDBX is straightforward. Here’s a simple example to help you get started:

from wdbx import WDBX

# Initialize the database
db = WDBX()

# Add data
db.add_vector("example_vector", [0.1, 0.2, 0.3])

# Query data
result = db.query_vector([0.1, 0.2, 0.3])
print(result)

This snippet demonstrates how to initialize the database, add a vector, and query it. For more complex queries and operations, refer to the documentation.

WDBX supports a variety of plugins to extend its functionality. Here’s how to create and use a plugin:

1. Create a new plugin file in the plugins directory.
2. Define your plugin class, implementing the required methods.
3. Load your plugin in the main application.

Example plugin structure:

class MyPlugin:
    def __init__(self):
        # Initialization code here

    def run(self):
        # Plugin functionality here

For a complete guide on creating plugins, check the documentation.

We welcome contributions! If you want to improve WDBX-Py, follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/YourFeature

3. Make your changes and commit them:

   git commit -m "Add some feature"

4. Push to the branch:

   git push origin feature/YourFeature

5. Open a pull request.

This project is licensed under the MIT License. See the LICENSE file for details.

To download the latest version of WDBX-Py, visit the Releases section. Make sure to execute the downloaded file as instructed.

For questions or support, feel free to reach out:

• Email: support@example.com
• Twitter: @WDBXOfficial

Thank you for checking out WDBX-Py! We hope you find it useful for your AI applications. For updates, keep an eye on our Releases section.