Semantic Kernel Concepts by Feature

This section contains code snippets that demonstrate the usage of Semantic Kernel features.

Features	Description
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
Logging	Showing how to set up logging
Memory	Using Memory AI concepts
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

1. Reading Environment Variables:

   • Primary Source: Pydantic first attempts to read the required settings from environment variables.

2. Using a .env File:

   • Fallback Source: If the required environment variables are not set, Pydantic will look for a .env file in the current working directory.
   • Custom Path (Optional): You can specify an alternative path for the .env file via env_file_path. This can be either a relative or an absolute path.

3. Direct Constructor Input:

   • As an alternative to environment variables and .env files, you can pass the required settings directly through the constructor of the AI Connector or Memory Connector.

Best Practices

• .env File Placement: We highly recommend placing the .env file in the semantic-kernel/python root 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.