mirror of
https://github.com/microsoft/semantic-kernel.git
synced 2026-10-12 02:19:06 +00:00
Abby Harrison
a9b61b2cf4
### Motivation and Context The release of Python SK from 0.2.4 -> 0.2.6 significantly increased the size of the pip package. The dependencies causing this are not core to the kernel, so this PR establishes a pattern for SK connector dependencies (ex: Hugging Face, memory storage) ### Description - Added a poetry group called hugging_face - this is the same name as the connectors namespace in the code. - Added dependency installs to the Hugging Face sample notebook. - removed Hugging Face dependencies from requirements.txt. - Added import errors for torch, transformers, and sentence-transformers in the hugging_face connector classes. - Added command `--with hugging_face` to integration test setup.
124 lines
3.5 KiB
Markdown
124 lines
3.5 KiB
Markdown
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](https://openai.com/api/) or
|
||
[Azure Open AI service key](https://learn.microsoft.com/azure/cognitive-services/openai/quickstart?pivots=rest-api)
|
||
|
||
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:
|
||
|
||
- [python/tests](tests)
|
||
- [samples/notebooks/python](../samples/notebooks/python).
|
||
|
||
# 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:
|
||
|
||
```bash
|
||
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.
|
||
|
||
```bash
|
||
# Install poetry package
|
||
pip3 install poetry
|
||
|
||
# Use poetry to install base project dependencies
|
||
poetry install
|
||
|
||
# If you want to use connectors such as hugging face
|
||
# poetry install --with <connector group name>
|
||
# example: poetry install --with hugging_face
|
||
|
||
# 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](tests/unit/) folder.
|
||
|
||
cd python
|
||
poetry install
|
||
poetry run pytest tests/unit
|
||
|
||
You can run the integration tests under the [tests/integration](tests/integration/) folder.
|
||
|
||
cd python
|
||
poetry install
|
||
poetry run pytest tests/integration
|
||
|
||
You can also run all the tests together under the [tests](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](../python) folder:
|
||
|
||
poetry run pre-commit run -c .conf/.pre-commit-config.yaml -a
|
||
|
||
## Running ruff
|
||
|
||
poetry run ruff check .
|