Skip to content

Latest commit

 

History

History
54 lines (40 loc) · 5.58 KB

README.md

File metadata and controls

54 lines (40 loc) · 5.58 KB
Raw

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 and OnnxGenAI connector to talk to models hosted locally in Ollama, OnnxGenAI 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
Structured Output How to leverage OpenAI's json_schema structured output functionality.
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.

Microsoft Entra Token Authentication

To authenticate to your Azure resources using a Microsoft Entra Authentication Token, the AzureChatCompletion AI Service connector now supports this as a built-in feature. If you do not provide an API key -- either through an environment variable, a .env file, or the constructor -- and you also do not provide a custom AsyncAzureOpenAI client, an ad_token, or an ad_token_provider, the AzureChatCompletion connector will attempt to retrieve a token using the DefaultAzureCredential.

To successfully retrieve and use the Entra Auth Token, you need the Cognitive Services OpenAI Contributor role assigned to your Azure OpenAI resource. By default, the https://cognitiveservices.azure.com token endpoint is used. You can override this endpoint by setting an environment variable .env variable as AZURE_OPENAI_TOKEN_ENDPOINT or by passing a new value to the AzureChatCompletion constructor as part of the AzureOpenAISettings.

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.