This release is a pre-release and may not be stable for production use.
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.
# 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 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.
1 maintainer
Metadata
1 maintainer
Release files for swarmauri_base 0.6.0.dev107
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Release files / swarmauri_base-0.6.0.dev107.tar.gz
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