Jordan Henkel
407fb36e59
### Motivation and Context The `python-preview` branch is under active development and many docs/tests/examples are getting a bit out of sync. There are also a few small cleanup chores/refactors to make _usage_ easier (e.g., to make examples cleaner). I've grouped these small fixes (plus a few bug fixes) into this "cleanup" PR. ### Description This PR does the following: 1. Updates the out-of-date example in `README.md` 2. Fixes all 5 of the notebooks we support right now (1,2,3,4, and 6; notebook 5 is planner, I've blanked out most of the code there so we don't confuse people, especially as the planner API is changing) 3. Fixes a bug w/ `stop_sequences` in `(Azure)OpenAITextCompletion` 4. Fixes a bug introduced in our upgrades to the embeddings cosine-sim check (we should add more thorough tests here) 5. Cleans up tests 6. Ensures end-to-end tests are all runnable 7. Fixes up the `kernel_extensions` to work more naturally (so that end users can use them directly on the `kernel` object and get good type-hinting)
2.3 KiB
Quickstart with Poetry
Installation
First, navigate to the directory containing this README using your chosen shell. You will need to have Python 3.10 installed.
Install the Poetry package manager and create a project virtual environment. (Note: we require at least Poetry 1.2.0 and Python 3.10.)
# Install poetry package
pip3 install poetry
# Use poetry to install project deps
poetry install
# Use poetry to activate project venv
poetry shell
Make sure you have an Open AI API Key or Azure Open AI service key
Copy those keys into a .env file in this repo (see the .env.example file):
OPENAI_API_KEY=""
OPENAI_ORG_ID=""
AZURE_OPENAI_API_KEY=""
AZURE_OPENAI_ENDPOINT=""
Quickstart ⚡
import semantic_kernel as sk
import semantic_kernel.ai.open_ai as sk_oai
kernel = sk.create_kernel()
# This requires a `.env` file in your current
# directory (see above)
api_key, org_id = sk.openai_settings_from_dot_env()
kernel.config.add_text_backend(
"davinci-002", sk_oai.OpenAITextCompletion(
"text-davinci-002", api_key, org_id
)
)
sk_prompt = """
{{$input}}
Give me the TLDR in exactly 5 words.
"""
text_to_summarize = """
1) A robot may not injure a human being or, through inaction,
allow a human being to come to harm.
2) A robot must obey orders given it by human beings except where
such orders would conflict with the First Law.
3) A robot must protect its own existence as long as such protection
does not conflict with the First or Second Law.
"""
tldr_function = kernel.create_semantic_function(
sk_prompt,
max_tokens=200,
temperature=0,
top_p=0.5,
)
summary = await kernel.run_on_str_async(text_to_summarize, tldr_function)
output = str(summary.variables).strip()
print("Output: " + output)
# Output: Robots must not harm humans.
Hint: if you want to run this via a file, say my_sk_example.py, you will
need to import asyncio and replace the await kernel.run_on_str_async(...)
call with asyncio.run(kernel.run_on_str_async(...)).
Hint: if you want to try this directly in your terminal, run python3 -m asyncio
and then paste the code above into the Python REPL. (This is to enable the
top-level await syntax.)