PyFuncAI 0.1.2

Prompt-defined Python functions with lazy or eager LLM-backed generation.

PyFuncAI

PyFuncAI is a Python library for defining functions with prompts and letting an LLM generate the implementation on demand.

It is built as a normal PyPI-style package, exposes a small public API, supports local Ollama models and remote OpenAI/Gemini providers, and can cache generated source on disk for reuse across runs.

Branch Strategy

The repository is set up around three long-lived branches:

• main: production and release branch, including PyPI deployment
• preview: pre-release validation branch
• dev: active development branch

The GitHub Actions workflows are wired to run CI on all three branches, while PyPI publishing is restricted to version tags that point to commits on main.

What It Does

PyFuncAI turns a prompt plus a Python signature into a callable object:

• mode="lazy" delays generation until first use.
• mode="eager" builds immediately.
• cache=True stores generated source on disk.
• .build() forces materialization ahead of time.
• .source exposes the generated Python source once available.

The generated code is validated before execution:

• exactly one top-level function must be returned
• the generated signature must exactly match the requested signature
• only approved standard-library imports are allowed
• obvious dangerous builtins such as open, eval, and exec are blocked

This is still experimental. Validation is intentionally conservative, but LLM-generated code should still be treated as untrusted.

Installation

Once published:

pip install pyfuncai

For local development from this repo:

pip install -e .[dev]

To run the minimal example directly from the repository checkout:

python example.py

Automation

GitHub Actions workflows live in .github/workflows/:

• ci.yml: formatting, tests, coverage, and package builds on main, preview, dev, and pull requests
• release.yml: verifies the tag commit is on main, builds distributions, creates a GitHub release, and publishes to PyPI

The coverage badge expects Codecov to be enabled for the repository. The CI workflow already uploads coverage.xml; once Codecov is connected, the badge will begin reporting real data.

Quick Start

Ollama

This was smoke-tested locally on March 13, 2026 with:

• Ollama at http://localhost:11434
• model qwen3.5:latest

from pyfuncai import connect, create_function

connect(
    "ollama",
    model="qwen3.5:latest",
    base_url="http://localhost:11434",
    timeout=180,
)

greet = create_function(
    "Return a short greeting for the provided name.",
    signature="(name: str) -> str",
    function_name="greet",
    mode="eager",
    cache=True,
)

print(greet("Alice"))
print(greet.source)

OpenAI

from pyfuncai import connect, create_function

connect(
    "openai",
    model="gpt-5-mini",
    api_key="YOUR_OPENAI_API_KEY",
)

slugify = create_function(
    "Convert text into a URL-friendly slug.",
    signature="(text: str) -> str",
    function_name="slugify",
)

OPENAI_API_KEY is also supported.

Gemini

from pyfuncai import connect, create_function

connect(
    "gemini",
    model="gemini-2.5-flash",
    api_key="YOUR_GEMINI_API_KEY",
)

summarize = create_function(
    "Summarize a short paragraph in one sentence.",
    signature="(text: str) -> str",
    function_name="summarize",
)

GEMINI_API_KEY is also supported.

Public API

connect(provider, **config)

Registers the default provider used by later create_function() calls.

Supported providers:

• ollama
• openai
• gemini

Common configuration keys:

• model
• timeout
• cache_dir

Provider-specific keys:

• Ollama: base_url
• OpenAI: api_key, base_url
• Gemini: api_key, base_url

create_function(...)

Main arguments:

• prompt
• signature
• cache=True
• mode="lazy" or mode="eager"
• function_name
• provider
• cache_dir
• system_prompt
• temperature
• max_output_tokens
• allow_modules

It returns a GeneratedFunction, which is callable and also exposes:

• .build(force=False)
• .source
• .cache_key
• .is_built

createFunction(...) is available as a compatibility alias.

Cache Behavior

Cached source is keyed by:

• prompt
• requested signature
• function name
• provider identity
• mode
• allowed module list

By default PyFuncAI uses an OS-appropriate user cache directory. You can override that globally in connect(..., cache_dir=...) or per function with create_function(..., cache_dir=...).

Live Testing

The unit test suite is offline and deterministic:

pytest

An optional live Ollama integration test is included but skipped by default:

PYFUNCAI_RUN_OLLAMA_TESTS=1 pytest tests/test_ollama_live.py

PowerShell:

$env:PYFUNCAI_RUN_OLLAMA_TESTS = "1"
pytest tests/test_ollama_live.py

Optional environment variables for the live test:

• PYFUNCAI_OLLAMA_MODEL
• PYFUNCAI_OLLAMA_BASE_URL
• PYFUNCAI_OLLAMA_TIMEOUT

Project Layout

.github/workflows/
  ci.yml
  release.yml
src/pyfuncai/
  __init__.py
  cache.py
  compiler.py
  core.py
  exceptions.py
  prompts.py
  providers.py
  validation.py
tests/
example.py

The package uses a src/ layout and pyproject.toml, which keeps it ready for normal wheel and sdist publishing.

Safety Notes

PyFuncAI validates generated code, but it does not provide hard sandboxing. The current implementation focuses on:

• AST checks
• restricted imports
• restricted builtins during execution

That helps, but it is not the same thing as secure isolation. Do not use generated code against secrets, production systems, or privileged environments without stronger containment.

Status

Current state of the repository:

• distributable package layout via pyproject.toml
• Ollama/OpenAI/Gemini provider adapters
• lazy and eager generation
• disk cache
• validation and restricted execution
• unit tests and optional live Ollama test
• CI, release automation, and PyPI publishing workflow

Publishing To PyPI

PyFuncAI is configured for PyPI trusted publishing with GitHub Actions. The release workflow file is:

• .github/workflows/release.yml

1. Configure GitHub

In the GitHub repository:

1. Create or confirm the long-lived branches: main, preview, and dev.
2. In Settings -> Environments, create an environment named pypi.
3. Optionally add protection rules so only approved maintainers can publish.
4. Push the workflow files to GitHub.

Recommended:

• protect main
• create tags like v0.1.0 only from main

2. Configure PyPI Trusted Publishing

In PyPI, add a new trusted publisher using the GitHub tab with these values:

• PyPI Project Name: pyfuncai
• Owner: AaronCreor
• Repository name: PyFuncAI
• Workflow name: release.yml
• Environment name: pypi

This matches the current repository and workflow layout.

3. Publish A Release

From a clean main branch state:

1. Update version in pyproject.toml.
2. Commit and push to main.
3. Create and push a version tag:

git checkout main
git pull
git tag v0.1.0
git push origin main
git push origin v0.1.0

That tag triggers release.yml, which will:

• verify the tag commit is reachable from main
• build the wheel and sdist
• create a GitHub Release with the built artifacts
• publish the package to PyPI through trusted publishing

4. Create The Extra Branches On GitHub

Local branches now exist, but GitHub will not see them until you push them:

git push -u origin preview
git push -u origin dev

References

• PyPI trusted publishing docs: https://docs.pypi.org/trusted-publishers/
• PyPA publishing guide: https://packaging.python.org/en/latest/tutorials/packaging-projects/
• GitHub Actions badges: https://docs.github.com/actions/monitoring-and-troubleshooting-workflows/monitoring-workflows/adding-a-workflow-status-badge

License

Apache-2.0