# Core Library

The Core Library provides the foundational interfaces and abstract base classes necessary for developing scalable and flexible machine learning agents, models, and tools. It is designed to offer a standardized approach to implementing various components of machine learning systems, such as models, parsers, conversations, and vector stores.

## Features

- **Models Interface**: Define and interact with predictive models.
- **Agents Interface**: Build and manage intelligent agents for varied tasks.
- **Tools Interface**: Develop tools with standardized execution and configuration.
- **Parsers and Conversations**: Handle and parse text data, manage conversations states.
- **Vector Stores**: Interface for vector storage and similarity searches.
- **Document Stores**: Manage the storage and retrieval of documents.
- **Retrievers and Chunkers**: Efficiently retrieve relevant documents and chunk large text data.

## Getting Started

To start developing with the Core Library, include it as a module in your Python project. Ensure you have Python 3.6 or later installed.

```python
# Example of using an abstract model interface from the Core Library
from swarmauri.core.models.IModel import IModel

class MyModel(IModel):
    # Implement the abstract methods here
    pass
```

## Documentation

For more detailed documentation on each interface and available abstract classes, refer to the [Docs](/docs) directory within the library.

## Contributing

Contributions are welcome! If you'd like to add a new feature, fix a bug, or improve documentation, please submit a pull request.

## License

See `LICENSE` for more information.
