Python: Provide full list of samples in concepts README. (#10161)

### Motivation and Context

Provide the full list of samples including hyperlinks in the
samples/concepts/README.md file instead of only the type of sample.

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### Description

This PR:
- Provides the full list of samples including hyperlinks in the
samples/concepts/README.md file instead of only the type of sample.
- Renames the "structured_output" folder to be "structured_outputs"
which is the name OpenAI uses.
- Closes #6928

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These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

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- [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 😄

python/samples/concepts/README.md

@@ -1,32 +1,157 @@
 # 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](../../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  |
+## Table of Contents
+
+### Agents - Creating and using [agents](../../semantic_kernel/agents/) in Semantic Kernel
+
+- [Assistant Agent Chart Maker](./agents/assistant_agent_chart_maker.py)
+- [Assistant Agent File Manipulation](./agents/assistant_agent_file_manipulation.py)
+- [Assistant Agent File Manipulation Streaming](./agents/assistant_agent_file_manipulation_streaming.py)
+- [Assistant Agent Retrieval](./agents/assistant_agent_retrieval.py)
+- [Assistant Agent Streaming](./agents/assistant_agent_streaming.py)
+- [Chat Completion Function Termination](./agents/chat_completion_function_termination.py)
+- [Mixed Chat Agents](./agents/mixed_chat_agents.py)
+- [Mixed Chat Agents Plugins](./agents/mixed_chat_agents_plugins.py)
+- [Mixed Chat Files](./agents/mixed_chat_files.py)
+- [Mixed Chat Reset](./agents/mixed_chat_reset.py)
+- [Mixed Chat Streaming](./agents/mixed_chat_streaming.py)
+
+### Audio - Using services that support audio-to-text and text-to-audio conversion
+
+- [Chat with Audio Input](./audio/01-chat_with_audio_input.py)
+- [Chat with Audio Output](./audio/02-chat_with_audio_output.py)
+- [Chat with Audio Input and Output](./audio/03-chat_with_audio_input_output.py)
+- [Audio Player](./audio/audio_player.py)
+- [Audio Recorder](./audio/audio_recorder.py)
+
+### AutoFunctionCalling - Using `Auto Function Calling` to allow function call capable models to invoke Kernel Functions automatically
+
+- [Azure Python Code Interpreter Function Calling](./auto_function_calling/azure_python_code_interpreter_function_calling.py)
+- [Function Calling with Required Type](./auto_function_calling/function_calling_with_required_type.py)
+- [Parallel Function Calling](./auto_function_calling/parallel_function_calling.py)
+- [Chat Completion with Auto Function Calling Streaming](./auto_function_calling/chat_completion_with_auto_function_calling_streaming.py)
+- [Functions Defined in JSON Prompt](./auto_function_calling/functions_defined_in_json_prompt.py)
+- [Chat Completion with Manual Function Calling Streaming](./auto_function_calling/chat_completion_with_manual_function_calling_streaming.py)
+- [Functions Defined in YAML Prompt](./auto_function_calling/functions_defined_in_yaml_prompt.py)
+- [Chat Completion with Auto Function Calling](./auto_function_calling/chat_completion_with_auto_function_calling.py)
+- [Chat Completion with Manual Function Calling](./auto_function_calling/chat_completion_with_manual_function_calling.py)
+- [Nexus Raven](./auto_function_calling/nexus_raven.py)
+
+### 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
+
+- [Simple Chatbot](./chat_completion/simple_chatbot.py)
+- [Simple Chatbot Kernel Function](./chat_completion/simple_chatbot_kernel_function.py)
+- [Simple Chatbot Logit Bias](./chat_completion/simple_chatbot_logit_bias.py)
+- [Simple Chatbot Store Metadata](./chat_completion/simple_chatbot_store_metadata.py)
+- [Simple Chatbot Streaming](./chat_completion/simple_chatbot_streaming.py)
+- [Simple Chatbot with Image](./chat_completion/simple_chatbot_with_image.py)
+
+### ChatHistory - Using and serializing the [`ChatHistory`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/contents/chat_history.py)
+
+- [Serialize Chat History](./chat_history/serialize_chat_history.py)
+
+### Filtering - Creating and using Filters
+
+- [Auto Function Invoke Filters](./filtering/auto_function_invoke_filters.py)
+- [Function Invocation Filters](./filtering/function_invocation_filters.py)
+- [Function Invocation Filters Stream](./filtering/function_invocation_filters_stream.py)
+- [Prompt Filters](./filtering/prompt_filters.py)
+- [Retry with Filters](./filtering/retry_with_filters.py)
+
+### 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)
+
+- [Kernel Arguments](./functions/kernel_arguments.py)
+
+### Grounding - An example of how to perform LLM grounding
+
+- [Grounded](./grounding/grounded.py)
+
+### 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
+
+- [ONNX Chat Completion](./local_models/onnx_chat_completion.py)
+- [LM Studio Text Embedding](./local_models/lm_studio_text_embedding.py)
+- [LM Studio Chat Completion](./local_models/lm_studio_chat_completion.py)
+- [ONNX Phi3 Vision Completion](./local_models/onnx_phi3_vision_completion.py)
+- [Ollama Chat Completion](./local_models/ollama_chat_completion.py)
+- [ONNX Text Completion](./local_models/onnx_text_completion.py)
+
+### Logging - Showing how to set up logging
+
+- [Setup Logging](./logging/setup_logging.py)
+
+### Memory - Using [`Memory`](https://github.com/microsoft/semantic-kernel/tree/main/dotnet/src/SemanticKernel.Abstractions/Memory) AI concepts
+
+- [Azure Cognitive Search Memory](./memory/azure_cognitive_search_memory.py)
+- [Memory Data Models](./memory/data_models.py)
+- [New Memory](./memory/new_memory.py)
+- [Pandas Memory](./memory/pandas_memory.py)
+
+### 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
+
+- [MMLU Model Evaluation](./model_as_a_service/mmlu_model_eval.py)
+
+### 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)
+
+- [Azure Chat GPT with Data API](./on_your_data/azure_chat_gpt_with_data_api.py)
+- [Azure Chat GPT with Data API Function Calling](./on_your_data/azure_chat_gpt_with_data_api_function_calling.py)
+- [Azure Chat GPT with Data API Vector Search](./on_your_data/azure_chat_gpt_with_data_api_vector_search.py)
+
+### Planners - Showing the uses of [`Planners`](https://github.com/microsoft/semantic-kernel/tree/main/python/semantic_kernel/planners)
+
+- [Sequential Planner](./planners/sequential_planner.py)
+- [OpenAI Function Calling Stepwise Planner](./planners/openai_function_calling_stepwise_planner.py)
+- [Azure OpenAI Function Calling Stepwise Planner](./planners/azure_openai_function_calling_stepwise_planner.py)
+
+### Plugins - Different ways of creating and using [`Plugins`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/functions/kernel_plugin.py)
+
+- [Azure Key Vault Settings](./plugins/azure_key_vault_settings.py)
+- [Azure Python Code Interpreter](./plugins/azure_python_code_interpreter.py)
+- [OpenAI Function Calling with Custom Plugin](./plugins/openai_function_calling_with_custom_plugin.py)
+- [Plugins from Directory](./plugins/plugins_from_dir.py)
+
+### Processes - Examples of using the [`Process Framework`](../../semantic_kernel/processes/)
+
+- [Cycles with Fan-In](./processes/cycles_with_fan_in.py)
+- [Nested Process](./processes/nested_process.py)
+
+### 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
+
+- [Template Language](./prompt_templates/template_language.py)
+- [Azure Chat GPT API Jinja2](./prompt_templates/azure_chat_gpt_api_jinja2.py)
+- [Load YAML Prompt](./prompt_templates/load_yaml_prompt.py)
+- [Azure Chat GPT API Handlebars](./prompt_templates/azure_chat_gpt_api_handlebars.py)
+- [Configuring Prompts](./prompt_templates/configuring_prompts.py)
+
+### RAG - Different ways of `RAG` (Retrieval-Augmented Generation)
+
+- [RAG with Text Memory Plugin](./rag/rag_with_text_memory_plugin.py)
+- [Self-Critique RAG](./rag/self-critique_rag.py)
+
+### Search - Using [`Search`](https://github.com/microsoft/semantic-kernel/tree/main/python/semantic_kernel/connectors/search) services information
+
+- [Bing Search Plugin](./search/bing_search_plugin.py)
+- [Bing Text Search](./search/bing_text_search.py)
+- [Bing Text Search as Plugin](./search/bing_text_search_as_plugin.py)
+- [Google Search Plugin](./search/google_search_plugin.py)
+- [Google Text Search as Plugin](./search/google_text_search_as_plugin.py)
+
+### Service Selector - Shows how to create and use a custom service selector class
+
+- [Custom Service Selector](./service_selector/custom_service_selector.py)
+
+### Setup - How to set up environment variables for Semantic Kernel
+
+- [OpenAI Environment Setup](./setup/openai_env_setup.py)
+- [Chat Completion Services](./setup/chat_completion_services.py)
+
+### Structured Outputs - How to leverage OpenAI's json_schema [`Structured Outputs`](https://platform.openai.com/docs/guides/structured-outputs) functionality
+
+- [JSON Structured Outputs](./structured_outputs/json_structured_outputs.py)
+- [JSON Structured Outputs Function Calling](./structured_outputs/json_structured_outputs_function_calling.py)
+
+### 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
+
+- [Text Completion Client](./local_models/onnx_text_completion.py)
 
 # Configuring the Kernel