Date: 2025 12-December 11

how to add suggestions and completion to my python library?

i have a public library in pypi, but when i use it in vscode is dont get the same suggestions as i do from other libraries.

there is an __init__.py

 oolong@CIT-36NZRL3  ~/dev/pyhabitat   dev  ls
Dockerfile            __main__.py      build_executable.py  pyhabitat           version.rc.template
Dockerfile.multi-dev  __pycache__      build_pyz.py         pyhabitat-build
LICENSE               build            dist                 pyhabitat.egg-info
README.md             build_docker.sh  docs                 pyproject.toml
 oolong@CIT-36NZRL3  ~/dev/pyhabitat   dev  ls pyhabitat
__init__.py  __pycache__  cli.py  demo.py  environment.py  report.py  system_info.py  version_info.py
 oolong@CIT-36NZRL3  ~/dev/pyhabitat   dev  cat pyhabitat/__init__.py
# ./__init__.py
from .version_info import get_package_version as version
from .system_info import SystemInfo
from .report import report
from .environment import (
    matplotlib_is_available_for_gui_plotting,
    matplotlib_is_available_for_headless_image_export,
    tkinter_is_available,
    in_repl,
    on_freebsd,
    on_linux,
    on_android,
    on_windows,
    on_wsl,
    on_apple,
    on_termux,
    on_pydroid,
    on_ish_alpine,
    as_pyinstaller,
    as_frozen,
    is_elf,
    is_pyz,
    is_windows_portable_executable,
    is_macos_executable,
    is_pipx,
    is_python_script,
    interactive_terminal_is_available,
    web_browser_is_available,
    edit_textfile,
    interp_path,
    main,
    user_darrin_deyoung,
    can_spawn_shell,
    is_ascii,
    is_binary,
    is_running_in_uvicorn
)

# Optional: Set __all__ for explicit documentation and cleaner imports
__all__ = [
    'matplotlib_is_available_for_gui_plotting',
    'matplotlib_is_available_for_headless_image_export',
    'tkinter_is_available',
    'in_repl',
    'on_termux',
    'on_pydroid',
    'on_wsl',
    'on_freebsd',
    'on_linux',
    'on_android',
    'on_windows',
    'on_apple',
    'on_ish_alpine',
    'as_pyinstaller',
    'as_frozen',
    'is_elf',
    'is_pyz',
    'is_windows_portable_executable',
    'is_macos_executable',
    'is_pipx',
    'is_python_script',
    'interactive_terminal_is_available',
    'is_running_in_uvicorn',
    'web_browser_is_available',
    'edit_textfile',
    'interp_path',
    'user_darrin_deyoung',
    'can_spawn_shell',
    'is_ascii',
    'is_binary',
    'main',
    'report',
    'is_running_in_uvicorn',
    'version',
    'SystemInfo'

]

__version__ = version()
 oolong@CIT-36NZRL3  ~/dev/pyhabitat   dev  cat pyproject.toml
# pyproject.toml

[build-system]
requires = ["setuptools>=61.0.0", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "pyhabitat"
version = "1.0.53"
authors = [
  { name="George Clayton Bennett", email="george.bennett@memphistn.gov" },
]
description = "A lightweight library for detecting system environment, GUI, and build properties."
readme = "README.md"
requires-python = ">=3.7"
license = "MIT"
keywords = ["environment", "os-detection", "build-system"]
classifiers=[
    "Programming Language :: Python :: 3",
    "Operating System :: OS Independent",
    "Intended Audience :: Developers",
    "Environment :: Console",
    "Topic :: Software Development :: Build Tools",
    "Topic :: Software Development :: Libraries :: Python Modules",
    "Topic :: Utilities",
]

dependencies = []

[project.scripts]
pyhabitat = "pyhabitat.cli:run_cli"

[tool.setuptools.packages.find]
where = ["."]
include = ["pyhabitat*"]


 oolong@CIT-36NZRL3  ~/dev/pyhabitat   dev 

This is a great question that gets to the heart of making a Python library user-friendly in modern development environments like VS Code.

The lack of suggestions and completion in VS Code for your public library, pyhabitat, is almost certainly related to how VS Code (and its underlying Python language server, typically Pylance) performs static analysis and type inference.

Here is a comprehensive guide on how to improve suggestions and completions for your library.

1. The Core Issue: Type Information

Python is dynamically typed, meaning type checking happens at runtime. However, IDEs like VS Code rely on static analysis (checking the code without running it) to provide smart suggestions (IntelliSense) and catch errors. For static analysis to work well, the code needs to provide clear type information.

Since you are already using from .module import name in your __init__.py, VS Code should be able to follow these imports. The most common missing pieces are type hints and docstrings.

🛠️ Solution A: Add Python Type Hints

The single most effective way to improve IntelliSense is by adding type hints to all function arguments and return values. Type hints provide the language server with explicit information about what kind of data to expect.

Example: Before (Implicit Typing)

In your pyhabitat/report.py (or wherever report is defined):

Python

def report(include_version=True, include_environment=False):
    # ... logic ...
    return output_string

Example: After (Explicit Typing with Hints)

Python

# Assuming 'report' returns a string and takes booleans
def report(
    include_version: bool = True,
    include_environment: bool = False
) -> str:
    # ... logic ...
    return output_string

🛠️ Solution B: Add Documentation (Docstrings)

VS Code uses the function's docstring to populate the information box that appears when you hover over a function or start typing its parameters.

• Docstring Format: Use a recognized format like NumPy or Google style. Pylance (the VS Code Python language server) is excellent at parsing these to extract parameter types, descriptions, and return values.

Example: Google Style Docstring

Python

def report(
    include_version: bool = True,
    include_environment: bool = False
) -> str:
    """Generates a system and environment report.

    Args:
        include_version: If True, includes the package version info.
        include_environment: If True, includes detailed environment checks.

    Returns:
        A string containing the formatted report.
    """
    # ... function logic ...
    return output_string

2. Using __all__ Correctly (Already Done)

You correctly have a __all__ list in your pyhabitat/__init__.py.

Python

# Optional: Set __all__ for explicit documentation and cleaner imports
__all__ = [
    # ... list of names ...
    'version',
    'SystemInfo'
]

What it does: The __all__ list tells the Python interpreter (and static analysis tools) exactly which names should be exposed when a user does a from pyhabitat import * and implicitly helps define the public API surface.

Action: You've done this correctly. Ensure every name you want suggested is in this list.

3. Advanced Method: Stub Files (.pyi)

If your library has complex logic or if you cannot add type hints directly to the source code (e.g., to maintain backward compatibility for older Python versions or because it's C/Rust compiled), you can use stub files (.pyi).

• A stub file is a Python file that only contains function/class signatures and type hints, but no implementation logic.

• The IDE treats the stub file as the source of truth for typing and suggestions.

Example for pyhabitat/environment.py

1. Create a file named pyhabitat/environment.pyi (note the .pyi extension).

2. In this file, you would redefine the function signatures with types:

Python

# pyhabitat/environment.pyi
# Only signatures and type hints
def matplotlib_is_available_for_gui_plotting() -> bool: ...
def on_linux() -> bool: ...
def interp_path(path: str) -> str: ...
# ... and so on for all other functions imported from environment.py

4. Final Checklist and Next Steps

If you follow the steps for Type Hints and Docstrings, you should see a significant improvement in the IntelliSense and completion features when users install and use your pyhabitat library.

Would you like me to help you draft the typed and documented versions for a few functions from your environment.py or report.py as an example?

which of these suggestions is the most likely culprit? i imagine pyi files are further down the list but i could be wrong

That's a very insightful follow-up question. You are correct in prioritizing the effort—we should start with the most likely and easiest fixes before moving to more complex solutions like stub files.

🔎 Most Likely Culprits (In Order)

Based on your library structure and the typical requirements of modern Python language servers (like Pylance used in VS Code), here is the order of likelihood:

🥇 Culprit #1: Missing Type Hints (The Strongest Suspect)

• Likelihood: Very High (90%).

• Reasoning: Without type hints, the language server often cannot reliably determine the return type of a function or the expected type of a variable after an assignment. When it can't figure out the type, it can't suggest the methods/attributes available on that type.

  • Example: If SystemInfo is a class, but its methods/properties don't have type hints, VS Code will struggle to suggest methods like .get_os() or .is_64bit() when the user types info = pyhabitat.SystemInfo(); info..

• Action: This should be your first priority. Start by adding type hints to all public functions and methods in your modules (environment.py, report.py, system_info.py, etc.).

🥈 Culprit #2: Missing or Poor Docstrings

• Likelihood: Medium to High (70%).

• Reasoning: While docstrings don't always block autocompletion, high-quality, structured docstrings (like Google or NumPy style) actually assist the type inference process for complex cases. More importantly, they provide the essential descriptive text that makes the suggestion valuable to the user (i.e., the parameter description, return value description, and what the function does).

• Action: Add comprehensive, structured docstrings immediately after implementing type hints.

🥉 Culprit #3: Over-reliance on Runtime Features

• Likelihood: Low (10%).

• Reasoning: If your library uses heavy metaprogramming, dynamic attribute assignment, or other features that only resolve at runtime (e.g., dynamically creating class methods based on an external file), the static analysis tools will be blind to those features. However, based on the function names in your __init__.py (like on_linux, is_elf, report), your library seems primarily based on standard functions/classes, which static analysis should handle.

• Action: If you are using any advanced runtime modification, consider using stub files (the .pyi approach) to manually define the type-stable public interface for the language server.

The Role of Stub Files (.pyi)

You are correct: .pyi stub files are typically the last resort for pure Python libraries.

Recommendation: Do not use .pyi files right now. Your library is pure Python, so using standard Type Hints directly in the source code (.py) is the modern, simpler, and standard approach. It gives you the same benefit as stub files without the maintenance overhead.

🚀 Recommended Action Plan

1. Prioritize Type Hints: Open your source files (system_info.py, report.py, environment.py, etc.) and add type hints to all public functions, class methods, and properties.

2. Add Docstrings: Ensure every public function/method has a quality docstring explaining what it does, its arguments, and what it returns.

3. Test: Reinstall your package locally and test the completion in VS Code.

Would you like me to walk through the implementation of type hints for your pyhabitat/environment.py imports, focusing on a few examples like on_linux and interp_path?

FIN