Description: Finalize the project for public consumption. This involves writing comprehensive user documentation, polishing the codebase to meet professional standards, and configuring the packaging metadata for the Python Package Index (PyPI). This is the "Productization" phase of Pathos AI.
Objectives:
Create a high-quality README.md that serves as the "front door" of the project.
Generate a full API reference from the Google-style docstrings.
Ensure the package is "installable" via pip with correct versioning.
Finalize the CONCEPTS.md "Project Bible" as an educational resource for users.
Tasks:
[ ] Write the README.md:
Include a clear "Quick Start" guide showing A* and Genetic Algorithm examples.
Add the Benchmark results from Issue #11 to show performance.
Include clear installation instructions and a description of the "Hybrid-Native" philosophy.
[ ] Docstring Audit:
Review all files (core.py, searching/, etc.) to ensure every public class and function has a Google-style docstring with Args, Returns, and Raises.
[ ] Finalize pyproject.toml:
Add project metadata: Description, Author, License (MIT/Apache), and Keywords.
Configure "Entry Points" if we want any command-line tools (like a pathos-bench command).
[ ] Create Examples Gallery:
Create an examples/ folder with well-commented scripts:
maze_solver.py (A*)
n_queens.py (CSP)
tictactoe_bot.py (Minimax/MCTS)
function_optimization.py (PSO/DE)
[ ] Build Check:
Run python -m build to ensure the source distribution and wheel are generated without errors.
Acceptance Criteria:
[ ] A user can run pip install . and then import pathos in a separate script without errors.
[ ] The README.md contains at least three code snippets that run "out of the box."
[ ] All tests pass with pytest, and mypy reports zero type errors.
[ ] The project structure is clean, with no temporary or junk files (__pycache__, .pytest_cache) tracked in Git.
Dependencies:
Issues #1 through #11 (The entire library must be functional).
Description: Finalize the project for public consumption. This involves writing comprehensive user documentation, polishing the codebase to meet professional standards, and configuring the packaging metadata for the Python Package Index (PyPI). This is the "Productization" phase of Pathos AI.
Objectives:
Tasks:
Acceptance Criteria:
Dependencies:
Issues #1 through #11 (The entire library must be functional).