# 📖 DigitalKin.ai - KinKernel
[](https://badge.fury.io/py/kin-kernel)
[](https://pypi.python.org/pypi/kin-kernel/)
[](https://codecov.io/gh/DigitalKin-ai/kin-kernel)
[](http://creativecommons.org/licenses/by-nc-sa/4.0/)
[](https://vision.digitalkin.ai)
[](https://discord.com/invite/yVstHVcx)
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:
```shell
pip install kin-kernel
```
Or clone the repository and install the dependencies:
```shell
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:
```shell
pip install -r requirements/dev.txt
```
## ✨ Linter
Execute linters:
```bash
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:
```python
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:
```python
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:
```shell
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 contact@digitalkin.ai.
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!
---
Kin-kernel © 2023 by DigitalKin is licensed under CC BY-NC-SA 4.0
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"description": "# \ud83d\udcd6 DigitalKin.ai - KinKernel\n\n[](https://badge.fury.io/py/kin-kernel)\n[](https://pypi.python.org/pypi/kin-kernel/)\n[](https://codecov.io/gh/DigitalKin-ai/kin-kernel)\n[](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n[](https://vision.digitalkin.ai)\n[](https://discord.com/invite/yVstHVcx)\nWelcome 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.\n\n## \ud83d\udc40 Overview\n\nThe 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.\n\n## \ud83d\udca1 Features\n\n- Abstract base classes for standardizing Cell creation\n- Response models for consistent communication\n- Helper methods for schema information access\n- Configuration management\n- Example Cell implementation\n\n## \ud83d\udee0\ufe0f Installation\n\nBefore installing the KinKernel, ensure you have Python installed on your system. This package requires Python 3.10 or higher.\n\nTo install the KinKernel\nWith pipy:\n\n```shell\npip install kin-kernel\n```\n\nOr clone the repository and install the dependencies:\n\n```shell\ngit clone https://github.com/DigitalKin/kin-kernel.git\ncd kin-kernel-kit\npip install -r requirements/prod.txt\n```\n\nFor development purposes, you may also want to install the development dependencies:\n\n```shell\npip install -r requirements/dev.txt\n```\n\n## \u2728 Linter\n\nExecute linters:\n\n```bash\n flake8 kinkernel\n black kinkernel --check --diff\n black kinkernel\n mypy kinkernel\n pylint kinkernel\n```\n\n## \ud83d\udcbb Usage\n\nTo create a new Cell, you'll need to subclass the `Cell` class provided in the kinKernel and implement the required methods and properties.\n\nHere's a simple example of a Cell that processes input data:\n\n```python\nfrom pydantic import BaseModel\n\nfrom kinkernel import Cell\nfrom kinkernel.config import ConfigModel, EnvVar\n\n\nclass MyInputModel(BaseModel):\n value1: int\n value2: str\n\n\nclass MyOutputModel(BaseModel):\n processed_value: int\n\n\nclass MyCell(Cell[MyInputModel, MyOutputModel]):\n role = \"Processor\"\n description = \"Processes input data\"\n input_format = MyInputModel\n output_format = MyOutputModel\n config = ConfigModel(\n env_vars=[\n EnvVar(key=\"ENV_VAR_1\", value=\"value1\"),\n EnvVar(key=\"ENV_VAR_2\", value=\"value2\"),\n ]\n )\n\n async def _execute(self, input_data: MyInputModel) -> MyOutputModel:\n # Process the input_data as needed\n exec_result = {\"processed_value\": input_data.value1 * 2}\n return MyOutputModel(**exec_result)\n```\n\nYou can then instantiate and execute your Cell as follows:\n\n```python\nmy_cell = MyCell()\ninput_data = MyInputModel(value1=10, value2=\"example\")\noutput_data = my_cell.execute(input_data)\nprint(output_data)\n```\n\nFor a more detailed example, refer to the `examples/simple_cell_example.py` file in the repository.\n\n## \ud83e\uddea Testing\n\nThe CDK comes with a set of unit tests to ensure that your Cells work as expected. To run the tests, execute the following command:\n\n```shell\npytest\n```\n\n## \ud83d\udc65 Contribution\n\nContributions to the KinKernel are welcome! If you have suggestions for improvements or find any issues, please open an issue on our GitHub repository.\n\n## \ud83e\udd17 Support\n\nIf you have any questions or need support with the KinKernel, please reach out to us at contact@digitalkin.ai.\n\nThank you for using the DigitalKin Cell Development Kit. We look forward to seeing the innovative Cells you'll create for the Internet of Agents!\n\n---\n\nKin-kernel \u00a9 2023 by DigitalKin is licensed under CC BY-NC-SA 4.0\n",
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