kin-kernel 0.0.5

kin-kernel contain the default templates of Cells that compose a Kin.

📖 DigitalKin.ai - KinKernel

Welcome to the DigitalKin KinKernel ! This package is designed to enable developers to create Cells, which are autonomous agents that can be integrated into the Internet of Agents (IoA) ecosystem powered by DigitalKin.

👀 Overview

The KinKernel provides a framework for creating and managing Cells. Each Cell represents a distinct autonomous agent with a specific role and behavior within the IoA. The KinKernel ensures that all Cells adhere to a standard interface and can communicate effectively within the ecosystem.

💡 Features

• Abstract base classes for standardizing Cell creation
• Response models for consistent communication
• Helper methods for schema information access
• Configuration management
• Example Cell implementation

🛠️ Installation

Before installing the KinKernel, ensure you have Python installed on your system. This package requires Python 3.10 or higher.

To install the KinKernel With pipy:

pip install kin-kernel

Or clone the repository and install the dependencies:

git clone https://github.com/DigitalKin/kin-kernel.git
cd kin-kernel-kit
pip install -r requirements/prod.txt

For development purposes, you may also want to install the development dependencies:

pip install -r requirements/dev.txt

✨ Linter

Execute linters:

   flake8 kinkernel
   black kinkernel --check --diff
   black kinkernel
   mypy kinkernel
   pylint kinkernel

💻 Usage

To create a new Cell, you'll need to subclass the Cell class provided in the kinKernel and implement the required methods and properties.

Here's a simple example of a Cell that processes input data:

from pydantic import BaseModel

from kinkernel import Cell
from kinkernel.config import ConfigModel, EnvVar


class MyInputModel(BaseModel):
    value1: int
    value2: str


class MyOutputModel(BaseModel):
    processed_value: int


class MyCell(Cell[MyInputModel, MyOutputModel]):
    role = "Processor"
    description = "Processes input data"
    input_format = MyInputModel
    output_format = MyOutputModel
    config = ConfigModel(
        env_vars=[
            EnvVar(key="ENV_VAR_1", value="value1"),
            EnvVar(key="ENV_VAR_2", value="value2"),
        ]
    )

    async def _execute(self, input_data: MyInputModel) -> MyOutputModel:
        # Process the input_data as needed
        exec_result = {"processed_value": input_data.value1 * 2}
        return MyOutputModel(**exec_result)

You can then instantiate and execute your Cell as follows:

my_cell = MyCell()
input_data = MyInputModel(value1=10, value2="example")
output_data = my_cell.execute(input_data)
print(output_data)

For a more detailed example, refer to the examples/simple_cell_example.py file in the repository.

🧪 Testing

The CDK comes with a set of unit tests to ensure that your Cells work as expected. To run the tests, execute the following command:

pytest

👥 Contribution

Contributions to the KinKernel are welcome! If you have suggestions for improvements or find any issues, please open an issue on our GitHub repository.

🤗 Support

If you have any questions or need support with the KinKernel, please reach out to us at [e-mail].

Thank you for using the DigitalKin Cell Development Kit. We look forward to seeing the innovative Cells you'll create for the Internet of Agents!

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Kin-kernel © 2023 by DigitalKin is licensed under CC BY-NC-SA 4.0