swarmauri_core 0.7.1.dev1

This repository includes core interfaces for the Swarmauri framework.

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Swarmauri Core

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

• LLMs Interface: Define and interact with predictive models.

class IPredict(ABC):
    """
    Interface focusing on the basic properties and settings essential for defining models.
    """

    @abstractmethod
    def predict(self, *args, **kwargs) -> any:
        """
        Generate predictions based on the input data provided to the model.
        """
        pass

    @abstractmethod
    async def apredict(self, *args, **kwargs) -> any:
        """
        Generate predictions based on the input data provided to the model.
        """
    ...

• Agents Interface: Build and manage intelligent agents for varied tasks.

class IAgent(ABC):
    @abstractmethod
    def exec(self, input_data: Optional[Any], llm_kwargs: Optional[Dict]) -> Any:
        """
        Executive method that triggers the agent's action based on the input data.
        """
        pass

• Tools Interface: Develop tools with standardized execution and configuration.

class ITool(ABC):
    @abstractmethod
    def call(self, *args, **kwargs):
        pass

    @abstractmethod
    def __call__(self, *args, **kwargs) -> Dict[str, Any]:
        pass

• Parsers and Conversations: Handle and parse text data, manage conversations states.

class IParser(ABC):
    """
    Abstract base class for parsers. It defines a public method to parse input data (str or Message) into documents,
    and relies on subclasses to implement the specific parsing logic through protected and private methods.
    """

    @abstractmethod
    def parse(self, data: Union[str, bytes, FilePath]) -> List[IDocument]:
        """
        Public method to parse input data (either a str or a Message) into a list of Document instances.

        This method leverages the abstract _parse_data method which must be
        implemented by subclasses to define specific parsing logic.
        """
        pass

• Vector Stores: Interface for vector storage and similarity searches.

class IVectorStore(ABC):
    """
    Interface for a vector store responsible for storing, indexing, and retrieving documents.
    """

    @abstractmethod
    def add_document(self, document: IDocument) -> None:
        """
        Stores a single document in the vector store.

        Parameters:
        - document (IDocument): The document to store.
        """
        pass

    @abstractmethod
    def add_documents(self, documents: List[IDocument]) -> None:
        """
        Stores multiple documents in the vector store.

        Parameters:
        - documents (List[IDocument]): The list of documents to store.
        """
        pass

    ...

• Document Stores: Manage the storage and retrieval of documents.

class IDocumentStore(ABC):
    """
    Interface for a Document Store responsible for storing, indexing, and retrieving documents.
    """

    @abstractmethod
    def add_document(self, document: IDocument) -> None:
        """
        Stores a single document in the document store.

        Parameters:
        - document (IDocument): The document to store.
        """
        pass

    @abstractmethod
    def add_documents(self, documents: List[IDocument]) -> None:
        """
        Stores multiple documents in the document store.

        Parameters:
        - documents (List[IDocument]): The list of documents to store.
        """
        pass

Getting Started

To start developing with the Core Library, include it as a module in your Python project. Ensure you have Python 3.10 or later installed.

Steps to install via pypi

pip install swarmauri_core

Usage Example

# Example of using an abstract model interface from the Core Library
from swarmauri_core.llms.IPredict import IPredict

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

Contributing

Contributions are welcome! If you'd like to add a new feature, fix a bug, or improve documentation, kindly go through the contributions guidelines first.