baicai-dev 0.1.0

Development package for Baicai

Baicai Dev

A (not) powerful Python package for AI workflow development, built on top of LangChain and LangGraph frameworks.

Overview

Baicai Dev is a development toolkit designed to streamline the creation and management of AI workflows. It provides a flexible framework for building, testing, and deploying AI-powered applications using LangChain and LangGraph.

Features

• Workflow development and management
• Integration with LangChain and LangGraph
• Support for multiple LLM providers
• Development and testing utilities
• Code quality and type checking tools

Requirements

• Python 3.10 or higher (but less than 3.12)
• Poetry for dependency management

Installation

1. Clone the repository:

git clone [repository-url]
cd baicai-dev

2. Install dependencies using Poetry:

MacOs > 1. The torchvision package is trying to use the lzma module. The lzma module depends on the _lzma C extension. To fix this, you need to install the XZ development libraries, which provide the required LZMA support, you can install this using Homebrew: brew install xz > 2. libomp.dylib for lightgbm, solve by brew install libomp

poetry install

Development Setup

1. Create and activate a virtual environment:

poetry shell

2. Install development dependencies:

poetry install --with dev

Project Structure

baicai-dev/
├── baicai_dev/     # Main package directory
├── tests/          # Test files
├── pyproject.toml  # Project configuration
└── README.md       # This file

Development Tools

The project uses several development tools to maintain code quality:

• pytest: For testing
• mypy: For static type checking
• ruff: For linting and code formatting

Code Style

The project follows strict code style guidelines:

• Line length: 120 characters
• Indentation: 4 spaces
• Quote style: Double quotes
• Type hints are required

Testing

Run tests using pytest:

pytest

License

This project is licensed under the GPL-3.0 License.

Authors

• Zhaoyang tech [e-mail]

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request

Keywords

• Python
• AI
• Workflow
• LangChain
• LangGraph

Example data

• Fisher, R. (1936). Iris [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C56C76.
• This dataset was obtained from the StatLib repository. https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html
• seaborn-data/titanic.csv https://github.com/mwaskom/seaborn-data/blob/master/titanic.csv
• Productivity Prediction of Garment Employees [Dataset]. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C51S6D.