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Dev Setup

This document describes how to setup your environment with Python and uv, if you're working on new features or a bug fix for Semantic Kernel, or simply want to run the tests included.

System setup

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.

Using uv

uv allows us to use SK from the local files, without worrying about paths, as if you had SK pip package installed.

To install SK and all the required tools in your system, first, navigate to the directory containing this DEV_SETUP using your chosen shell.

For windows (non-WSL)

Check the uv documentation for the installation instructions. At the time of writing this is the command to install uv:

powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

You can then run the following commands manually:

# Install Python 3.10, 3.11, and 3.12
uv python install 3.10 3.11 3.12
# Create a virtual environment with Python 3.10 (you can change this to 3.11 or 3.12)
$PYTHON_VERSION = "3.10"
uv venv --python $PYTHON_VERSION
# Install SK and all dependencies
uv sync --all-extras --dev
# Install pre-commit hooks
uv run pre-commit install -c python/.pre-commit-config.yaml

Or you can then either install make and then follow the guide for Mac and Linux, or run the following commands, the commands are shown as bash but should work in powershell as well.

For Mac and Linux (both native and WSL)

It is super simple to get started, run the following commands:

make install

This will install uv, python, Semantic Kernel and all dependencies and the pre-commit config. It uses python 3.10 by default, if you want to change that set the PYTHON_VERSION environment variable to the desired version (currently supported are 3.10, 3.11, 3.12). For instance for 3.12"

make install PYTHON_VERSION=3.12

If you want to change python version (without installing uv, python and pre-commit), you can use the same parameter, but do:

make install-sk PYTHON_VERSION=3.12

ℹ️ Note: Running the install or install-sk command will wipe away your existing virtual environment and create a new one.

Alternatively you can run the VSCode task Python: Install to run the same command.

VSCode Setup

Open the workspace in VSCode.

The workspace for python should be rooted in the ./python folder.

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 (default path is .venv) created by uv is selected.

If prompted, install ruff. (It should have been installed as part of uv sync --dev).

You also need to install the ruff extension in VSCode so that auto-formatting uses the ruff formatter on save. Read more about the extension here.

LLM setup

Make sure you have an OpenAI API Key or Azure OpenAI service key

There are two methods to manage keys, secrets, and endpoints:

  1. Store them in environment variables. SK Python leverages pydantic settings to load keys, secrets, and endpoints from the environment.

    When you are using VSCode and have the python extension setup, it automatically loads environment variables from a .env file, so you don't have to manually set them in the terminal. During runtime on different platforms, environment settings set as part of the deployments should be used.

  2. Store them in a separate .env file, like dev.env, you can then pass that name into the constructor for most services, to the env_file_path parameter, see below.

    Do not store *.env files in your repository, and make sure to add them to your .gitignore file.

There are a lot of settings, for a more extensive list of settings, see ALL_SETTINGS.md.

Example for file-based setup with OpenAI Chat Completions

To configure a .env file with just the keys needed for OpenAI Chat Completions, you can create a openai.env (this name is just as an example, a single .env with all required keys is more common) file in the root of the python folder with the following content:

Content of openai.env:

OPENAI_API_KEY=""
OPENAI_CHAT_MODEL_ID="gpt-4o-mini"

You will then configure the ChatCompletion class with the keyword argument env_file_path:

chat_completion = OpenAIChatCompletion(service_id="test", env_file_path="openai.env")

Tests

You can run the unit tests under the tests/unit folder.

    uv run pytest tests/unit

Alternatively, you can run them using VSCode Tasks. Open the command palette (Ctrl+Shift+P) and type Tasks: Run Task. Select Python: Tests - Unit or Python: Tests - Code Coverage from the list.

You can run the integration tests under the tests/integration folder.

    uv run pytest tests/integration

You can also run all the tests together under the tests folder.

    uv run pytest tests

Alternatively, you can run them using VSCode Tasks. Open the command palette (Ctrl+Shift+P) and type Tasks: Run Task. Select Python: Tests - All from the list.

Implementation Decisions

Asynchronous programming

It's important to note that most of this library is written with asynchronous in mind. The developer should always assume everything is asynchronous. One can use the function signature with either async def or def to understand if something is asynchronous or not.

Documentation

Each file should have a single first line containing: # Copyright (c) Microsoft. All rights reserved.

We follow the Google Docstring style guide for functions and methods. They are currently not checked for private functions (functions starting with '_').

They should contain:

  • Single line explaining what the function does, ending with a period.
  • If necessary to further explain the logic a newline follows the first line and then the explanation is given.
  • The following three sections are optional, and if used should be separated by a single empty line.
  • Arguments are then specified after a header called Args:, with each argument being specified in the following format:
    • arg_name: Explanation of the argument.
    • if a longer explanation is needed for a argument, it should be placed on the next line, indented by 4 spaces.
    • Type and default values do not have to be specified, they will be pulled from the definition.
  • Returns are specified after a header called Returns: or Yields:, with the return type and explanation of the return value.
  • Finally, a header for exceptions can be added, called Raises:, with each exception being specified in the following format:
    • ExceptionType: Explanation of the exception.
    • if a longer explanation is needed for a exception, it should be placed on the next line, indented by 4 spaces.

Putting them all together, gives you at minimum this:

def equal(arg1: str, arg2: str) -> bool:
    """Compares two strings and returns True if they are the same."""
    ...

Or a complete version of this:

def equal(arg1: str, arg2: str) -> bool:
    """Compares two strings and returns True if they are the same.

    Here is extra explanation of the logic involved.

    Args:
        arg1: The first string to compare.
        arg2: The second string to compare.
            This string requires extra explanation.

    Returns:
        True if the strings are the same, False otherwise.

    Raises:
        ValueError: If one of the strings is empty.
    """
    ...

If in doubt, use the link above to read much more considerations of what to do and when, or use common sense.

Pydantic and Serialization

This section describes how one can enable serialization for their class using Pydantic. For more info you can refer to the Pydantic Documentation.

Upgrading existing classes to use Pydantic

Let's take the following example:

class A:
    def __init__(self, a: int, b: float, c: List[float], d: dict[str, tuple[float, str]] = {}):
        self.a = a
        self.b = b
        self.c = c
        self.d = d

You would convert this to a Pydantic class by sub-classing from the KernelBaseModel class.

from pydantic import Field
from semantic_kernel.kernel_pydantic import KernelBaseModel

class A(KernelBaseModel):
    # The notation for the fields is similar to dataclasses.
    a: int
    b: float
    c: list[float]
    # Only, instead of using dataclasses.field, you would use pydantic.Field
    d: dict[str, tuple[float, str]] = Field(default_factory=dict)

Classes with data that need to be serialized, and some of them are Generic types

Let's take the following example:

from typing import TypeVar

T1 = TypeVar("T1")
T2 = TypeVar("T2", bound=<some class>)

class A:
    def __init__(a: int, b: T1, c: T2):
        self.a = a
        self.b = b
        self.c = c

You can use the KernelBaseModel to convert these to pydantic serializable classes.

from typing import Generic, TypeVar

from semantic_kernel.kernel_pydantic import KernelBaseModel

T1 = TypeVar("T1")
T2 = TypeVar("T2", bound=<some class>)

class A(KernelBaseModel, Generic[T1, T2]):
    # T1 and T2 must be specified in the Generic argument otherwise, pydantic will
    # NOT be able to serialize this class
    a: int
    b: T1
    c: T2

Code quality checks

To run the same checks that run during a commit and the GitHub Action Python Code Quality Checks, you can use this command, from the python folder:

    uv run pre-commit run -a

or use the following task (using Ctrl+Shift+P):

  • Python - Run Checks to run the checks on the whole project.
  • Python - Run Checks - Staged to run the checks on the currently staged files only.

Ideally you should run these checks before committing any changes, when you install using the instructions above the pre-commit hooks should be installed already.

Code Coverage

We try to maintain a high code coverage for the project. To run the code coverage on the unit tests, you can use the following command:

    uv run pytest --cov=semantic_kernel --cov-report=term-missing:skip-covered tests/unit/

or use the following task (using Ctrl+Shift+P):

  • Python: Tests - Code Coverage to run the code coverage on the whole project.

This will show you which files are not covered by the tests, including the specific lines not covered. Make sure to consider the untested lines from the code you are working on, but feel free to add other tests as well, that is always welcome!

Catching up with the latest changes

There are many people committing to Semantic Kernel, so it is important to keep your local repository up to date. To do this, you can run the following commands:

    git fetch upstream main
    git rebase upstream/main
    git push --force-with-lease

or:

    git fetch upstream main
    git merge upstream/main
    git push

This is assuming the upstream branch refers to the main repository. If you have a different name for the upstream branch, you can replace upstream with the name of your upstream branch.

After running the rebase command, you may need to resolve any conflicts that arise. If you are unsure how to resolve a conflict, please refer to the GitHub's documentation on resolving conflicts, or for VSCode.