Abby Harrison
05c586b9d8
### Motivation and Context Run python integration tests on every push to main, and every 12 hours. These integration tests will run on every push to main as well as twice a day. At the time of this PR, we are seeing ~3 PRs to the python SK per day. Each Integration test run will make ~32 AI requests. For each PR, 12 runs will be made: 3 each for python 3.8, 3.9, 3.10, and 3.11 for a total of ~400 calls per push to main. We should re-evaluate the trigger on pushes to main if PRs to the python SK significantly increase. ### Description - add workflow definition for integration tests - Example run: https://github.com/microsoft/semantic-kernel/actions/runs/4874609026/jobs/8695771345?pr=763 - For push triggers, add path checks for dotnet/** or python/** depending on the type of workflow - pinned requirements.txt dependencies to the versions pinned in the pyproject.toml file to ensure parity between pip package dependencies and local code **Note**: Integration tests are failing on windows with python 3.8 - already captured a task to address the problem
3.4 KiB
This document describes how to setup your environment with Python and Poetry, if you're working on new features or a bug fix for Semantic Kernel, or simply want to run the tests included.
LLM setup
Make sure you have an Open AI API Key or Azure Open AI service key
Copy those keys into a .env file (see the .env.example file):
OPENAI_API_KEY=""
OPENAI_ORG_ID=""
AZURE_OPENAI_DEPLOYMENT_NAME=""
AZURE_OPENAI_ENDPOINT=""
AZURE_OPENAI_API_KEY=""
We suggest adding a copy of the .env file under these folders:
System setup
To get started, you'll need VSCode and a local installation of Python 3.8+.
You can run:
python3 --version ; pip3 --version ; code -v
to verify that you have the required dependencies.
If you're on WSL
Check that you've cloned the repository to ~/workspace or a similar folder.
Avoid /mnt/c/ and prefer using your WSL user's home directory.
Ensure you have the WSL extension for VSCode installed (and the Python extension for VSCode installed).
You'll also need pip3 installed. If you don't yet have a python3 install in WSL,
you can run:
sudo apt-get update && sudo apt-get install python3 python3-pip
ℹ️ Note: if you don't have your PATH setup to find executables installed by pip3,
you may need to run ~/.local/bin/poetry install and ~/.local/bin/poetry shell
instead. You can fix this by adding export PATH="$HOME/.local/bin:$PATH" to
your ~/.bashrc and closing/re-opening the terminal._
Using Poetry
Poetry allows to use SK from the local files, without worrying about paths, as if you had SK pip package installed.
To install Poetry in your system, first, navigate to the directory containing this README using your chosen shell. You will need to have Python 3.8+ installed.
Install the Poetry package manager and create a project virtual environment. Note: SK requires at least Poetry 1.2.0.
# Install poetry package
pip3 install poetry
# Use poetry to install project deps
poetry install
# Use poetry to activate project venv
poetry shell
VSCode Setup
Open any of the .py files in the project and run the Python: Select Interpreter
command from the command palette. Make sure the virtual env (venv) created by
poetry is selected.
The python you're looking for should be under ~/.cache/pypoetry/virtualenvs/semantic-kernel-.../bin/python.
If prompted, install black and flake8 (if VSCode doesn't find those packages,
it will prompt you to install them).
Tests
You can run the unit tests under the tests/unit folder.
cd python
poetry install
poetry run pytest tests/unit
You can run the integration tests under the tests/integration folder.
cd python
poetry install
poetry run pytest tests/integration
You can also run all the tests together under the tests folder.
cd python
poetry install
poetry run pytest tests
Tools and scripts
Pipeline checks
To run the same checks that run during the GitHub Action build, you can use this command, from the python folder:
poetry run pre-commit run -c .conf/.pre-commit-config.yaml -a
Running ruff
poetry run ruff check .