# Standard Library


The Standard Library extends the Core Library with concrete implementations of models, agents, tools, parsers, and more. It aims to provide ready-to-use components that can be easily integrated into machine learning projects.

## Features

- **Predefined Models and Agents**: Implements standard models and agents ready for use.
- **Toolkit**: A collection of tools for various tasks (e.g., weather information, math operations).
- **Parsers Implementations**: Various parsers for text data, including HTML and CSV parsers.
- **Conversations and Chunkers**: Manage conversation histories and chunk text data.
- **Vectorizers**: Transform text data into vector representations.
- **Document Stores and Vector Stores**: Concrete implementations for storing and retrieving data.

## Getting Started

To make the best use of the Standard Library, first ensure that the Core Library is set up in your project as the Standard Library builds upon it.

```python
# Example usage of a concrete model from the Standard Library
from swarmauri.standard.models.concrete import OpenAIModel

# Initialize the model with necessary configuration
model = OpenAIModel(api_key="your_api_key_here")
```

## Documentation

For more detailed guides and API documentation, check the [Docs](/docs) directory within the library. You'll find examples, configuration options, and best practices for utilizing the provided components.

## Contributing

Your contributions can help the Standard Library grow! Whether it's adding new tools, improving models, or writing documentation, we appreciate your help. Please send a pull request with your contributions.

## License

Please see the `LICENSE` file in the repository for details.