ℹ️ NOTE: The Python SDK for Semantic Kernel is currently in preview. While most of the features available in the C# SDK have been ported, there may be bugs and we're working on some features still - these will come into the repo soon. We are also actively working on improving the code quality and developer experience, and we appreciate your support, input and PRs!
ℹ️ NOTE: This project is in early alpha and, just like AI, will evolve quickly. We invite you to join us in developing the Semantic Kernel together! Please contribute by using GitHub Discussions, opening GitHub Issues, sending us PRs.
Semantic Kernel (SK) is a lightweight SDK enabling integration of AI Large Language Models (LLMs) with conventional programming languages. The SK extensible programming model combines natural language semantic functions, traditional code native functions, and embeddings-based memory unlocking new potential and adding value to applications with AI.
SK supports prompt templating, function chaining, vectorized memory, and intelligent planning capabilities out of the box.
Semantic Kernel is designed to support and encapsulate several design patterns from the latest in AI research, such that developers can infuse their applications with complex skills like prompt chaining, recursive reasoning, summarization, zero/few-shot learning, contextual memory, long-term memory, embeddings, semantic indexing, planning, and accessing external knowledge stores as well as your own data.
By joining the SK community, you can build AI-first apps faster and have a front-row peek at how the SDK is being built. SK has been released as open-source so that more pioneering developers can join us in crafting the future of this landmark moment in the history of computing.
Here is a quick example of how to use Semantic Kernel from a Python script.
- Clone the repo and install SK dependencies, e.g. using
pip install -r python/requirements.txt - Create a new Python file and add the code below.
- Copy and paste your OpenAI API key in the script (support for Azure OpenAI is work in progress).
- Using Python 3.8 or later, run the code. Enjoy!
import sys
sys.path.append("./python") # nopep8
import semantic_kernel as sk
import asyncio
kernel = sk.create_kernel()
kernel.config.add_openai_completion_backend(
"davinci",
"text-davinci-003",
"...OpenAI Key...")
sk_prompt = """
{{$input}}
Give me the TLDR in 5 words.
"""
tldr_function = sk.extensions.create_semantic_function(
kernel,
sk_prompt,
max_tokens=200,
temperature=0,
top_p=0.5)
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.
"""
summary = asyncio.run(kernel.run_on_str_async(text_to_summarize, tldr_function))
print(f"Output: {summary}")
# Output: Robots must not harm humans.Refer to the FEATURE_PARITY.md doc to see where things stand in matching the features and functionality of the main SK branch.
We welcome your contributions and suggestions to SK community! One of the easiest ways to participate is to engage in discussions in the GitHub repository. Bug reports and fixes are welcome!
For new features, components, or extensions, please open an issue and discuss with us before sending a PR. This is to avoid rejection as we might be taking the core in a different direction, but also to consider the impact on the larger ecosystem.
To learn more and get started:
- Read the documentation
- Learn how to contribute to the project
- Join the Discord community
- Hear from the team on our blog
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT license.