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Evan Mattson
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### Motivation and Context Bump Python version to 1.3.0 from 1.2.0 for a release. <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> ### Description Bump Python version to 1.3.0 from 1.2.0 for a release. <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [X] The code builds clean without any errors or warnings - [X] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [X] All unit tests pass, and I have added new tests where possible - [X] I didn't break anyone 😄
Semantic Kernel Concepts by Feature
This section contains code snippets that demonstrate the usage of Semantic Kernel features.
| Features | Description |
|---|---|
| Agents | Creating and using agents in Semantic Kernel |
| AutoFunctionCalling | Using Auto Function Calling to allow function call capable models to invoke Kernel Functions automatically |
| ChatCompletion | Using ChatCompletion messaging capable service with models |
| Filtering | Creating and using Filters |
| Functions | Invoking Method or Prompt functions with Kernel |
| Grounding | An example of how to perform LLM grounding |
| Local Models | Using the OpenAI connector to talk to models hosted locally in Ollama and LM Studio |
| Logging | Showing how to set up logging |
| Memory | Using Memory AI concepts |
| Model-as-a-Service | Using models deployed as serverless APIs on Azure AI Studio to benchmark model performance against open-source datasets |
| On Your Data | Examples of using AzureOpenAI On Your Data |
| Planners | Showing the uses of Planners |
| Plugins | Different ways of creating and using Plugins |
| PromptTemplates | Using Templates with parametrization for Prompt rendering |
| RAG | Different ways of RAG (Retrieval-Augmented Generation) |
| Search | Using search services information |
| Service Selector | Shows how to create and use a custom service selector class. |
| Setup | How to setup environment variables for Semantic Kernel |
| TextGeneration | Using TextGeneration capable service with models |
Configuring the Kernel
In Semantic Kernel for Python, we leverage Pydantic Settings to manage configurations for AI and Memory Connectors, among other components. Here’s a clear guide on how to configure your settings effectively:
Steps for Configuration
-
Reading Environment Variables:
- Primary Source: Pydantic first attempts to read the required settings from environment variables.
-
Using a .env File:
- Fallback Source: If the required environment variables are not set, Pydantic will look for a
.envfile in the current working directory. - Custom Path (Optional): You can specify an alternative path for the
.envfile viaenv_file_path. This can be either a relative or an absolute path.
- Fallback Source: If the required environment variables are not set, Pydantic will look for a
-
Direct Constructor Input:
- As an alternative to environment variables and
.envfiles, you can pass the required settings directly through the constructor of the AI Connector or Memory Connector.
- As an alternative to environment variables and
Best Practices
- .env File Placement: We highly recommend placing the
.envfile in thesemantic-kernel/pythonroot directory. This is a common practice when developing in the Semantic Kernel repository.
By following these guidelines, you can ensure that your settings for various components are configured correctly, enabling seamless functionality and integration of Semantic Kernel in your Python projects.