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

| Features | Description |
| -------- | ----------- |
| Agents | Creating and using [agents](../../semantic_kernel/agents/) in Semantic Kernel |
| Audio | Using services that support audio-to-text and text-to-audio conversion |
| AutoFunctionCalling | Using `Auto Function Calling` to allow function call capable models to invoke Kernel Functions automatically |
| ChatCompletion | Using [`ChatCompletion`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/chat_completion_client_base.py) messaging capable service with models |
| ChatHistory | Using and serializing the [`ChatHistory`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/contents/chat_history.py) |
| Filtering | Creating and using Filters |
| Functions | Invoking [`Method`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/functions/kernel_function_from_method.py) or [`Prompt`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/functions/kernel_function_from_prompt.py) functions with [`Kernel`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/kernel.py) |
| Grounding | An example of how to perform LLM grounding |
| Local Models | Using the [`OpenAI connector`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/open_ai/services/open_ai_chat_completion.py) and [`OnnxGenAI connector`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_chat_completion.py) to talk to models hosted locally in Ollama, OnnxGenAI and LM Studio |
| Logging | Showing how to set up logging |
| Memory | Using [`Memory`](https://github.com/microsoft/semantic-kernel/tree/main/dotnet/src/SemanticKernel.Abstractions/Memory) AI concepts |
| Model-as-a-Service | Using models deployed as [`serverless APIs on Azure AI Studio`](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless?tabs=azure-ai-studio) to benchmark model performance against open-source datasets |
| On Your Data | Examples of using AzureOpenAI [`On Your Data`](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/use-your-data?tabs=mongo-db) |
| Planners | Showing the uses of [`Planners`](https://github.com/microsoft/semantic-kernel/tree/main/python/semantic_kernel/planners) |
| Plugins | Different ways of creating and using [`Plugins`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/functions/kernel_plugin.py) |
| Processes | Examples of using the [`Process Framework`](../../semantic_kernel/processes/) |
| PromptTemplates | Using [`Templates`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/prompt_template/prompt_template_base.py) 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`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/text_completion_client_base.py) capable service with models |
| Features | Description |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Agents | Creating and using [agents](../../semantic_kernel/agents/) in Semantic Kernel |
| Audio | Using services that support audio-to-text and text-to-audio conversion |
| AutoFunctionCalling | Using `Auto Function Calling` to allow function call capable models to invoke Kernel Functions automatically |
| ChatCompletion | Using [`ChatCompletion`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/chat_completion_client_base.py) messaging capable service with models |
| ChatHistory | Using and serializing the [`ChatHistory`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/contents/chat_history.py) |
| Filtering | Creating and using Filters |
| Functions | Invoking [`Method`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/functions/kernel_function_from_method.py) or [`Prompt`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/functions/kernel_function_from_prompt.py) functions with [`Kernel`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/kernel.py) |
| Grounding | An example of how to perform LLM grounding |
| Local Models | Using the [`OpenAI connector`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/open_ai/services/open_ai_chat_completion.py) and [`OnnxGenAI connector`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_chat_completion.py) to talk to models hosted locally in Ollama, OnnxGenAI and LM Studio |
| Logging | Showing how to set up logging |
| Memory | Using [`Memory`](https://github.com/microsoft/semantic-kernel/tree/main/dotnet/src/SemanticKernel.Abstractions/Memory) AI concepts |
| Model-as-a-Service | Using models deployed as [`serverless APIs on Azure AI Studio`](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless?tabs=azure-ai-studio) to benchmark model performance against open-source datasets |
| On Your Data | Examples of using AzureOpenAI [`On Your Data`](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/use-your-data?tabs=mongo-db) |
| Planners | Showing the uses of [`Planners`](https://github.com/microsoft/semantic-kernel/tree/main/python/semantic_kernel/planners) |
| Plugins | Different ways of creating and using [`Plugins`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/functions/kernel_plugin.py) |
| Processes | Examples of using the [`Process Framework`](../../semantic_kernel/processes/) |
| PromptTemplates | Using [`Templates`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/prompt_template/prompt_template_base.py) with parametrization for `Prompt` rendering |
| RAG | Different ways of `RAG` (Retrieval-Augmented Generation) |
| Reasoning | Using [`ChatCompletion`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/chat_completion_client_base.py) to reason with OpenAI Reasoning Model |
| 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`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/text_completion_client_base.py) capable service with models |

# Configuring the Kernel

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