Python: Restructure samples into new folders to make things more clea…

…r. (microsoft#6116)

### Motivation and Context

All previous samples were either in kernel syntax or a separate
notebooks folder. The goal is to make the samples easier to understand
and have a better structure.

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### Description

The PR restructures the kernel syntax examples into new folders:
concepts (with subfolders for previous syntax examples), demos,
getting_started, and learn_resources.
- Closes microsoft#6119 
- Adds a new concept/function example for understanding kernel
arguments.
- Updates the bookings plugin

<!-- 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 😄

README.md

@@ -90,7 +90,7 @@ The fastest way to learn how to use Semantic Kernel is with our C# and Python Ju
 demonstrate how to use Semantic Kernel with code snippets that you can run with a push of a button.
 
 - [Getting Started with C# notebook](dotnet/notebooks/00-getting-started.ipynb)
-- [Getting Started with Python notebook](python/notebooks/00-getting-started.ipynb)
+- [Getting Started with Python notebook](python/samples/getting_started/00-getting-started.ipynb)
 
 Once you've finished the getting started notebooks, you can then check out the main walkthroughs
 on our Learn site. Each sample comes with a completed C# and Python project that you can run locally.

python/DEV_SETUP.md

@@ -23,7 +23,7 @@ AZURE_OPENAI_API_KEY=""
 We suggest adding a copy of the `.env` file under these folders:
 
 - [python/tests](tests)
-- [./notebooks](./notebooks).
+- [./samples/getting_started](./samples/getting_started).
 
 ## System setup
 
@@ -133,12 +133,12 @@ Alternatively, you can run them using VSCode Tasks. Open the command palette
 
 ## Tools and scripts
 
-## Implementation Decisions 
+## Implementation Decisions
 
 ### Asynchronous programming
 
-It's important to note that most of this library is written with asynchronous in mind. The 
-developer should always assume everything is asynchronous. One can use the function signature 
+It's important to note that most of this library is written with asynchronous in mind. The
+developer should always assume everything is asynchronous. One can use the function signature
 with either `async def` or `def` to understand if something is asynchronous or not.
 
 ## Pydantic and Serialization

python/README.md

@@ -148,18 +148,18 @@ get started with the Semantic Kernel.
 
 Python notebooks:
 
-- [Getting started with Semantic Kernel](./notebooks/00-getting-started.ipynb)
-- [Loading and configuring Semantic Kernel](./notebooks/01-basic-loading-the-kernel.ipynb)
-- [Running AI prompts from file](./notebooks/02-running-prompts-from-file.ipynb)
-- [Creating Prompt Functions at runtime (i.e. inline functions)](./notebooks/03-prompt-function-inline.ipynb)
-- [Using Context Variables to Build a Chat Experience](./notebooks/04-kernel-arguments-chat.ipynb)
-- [Introduction to planners](./notebooks/05-using-the-planner.ipynb)
-- [Building Memory with Embeddings](./notebooks/06-memory-and-embeddings.ipynb)
-- [Using Hugging Face for Plugins](./notebooks/07-hugging-face-for-plugins.ipynb)
-- [Combining native functions and semantic functions](./notebooks/08-native-function-inline.ipynb)
-- [Groundedness Checking with Semantic Kernel](./notebooks/09-groundedness-checking.ipynb)
-- [Returning multiple results per prompt](./notebooks/10-multiple-results-per-prompt.ipynb)
-- [Streaming completions with Semantic Kernel](./notebooks/11-streaming-completions.ipynb)
+- [Getting started with Semantic Kernel](./samples/getting_started/00-getting-started.ipynb)
+- [Loading and configuring Semantic Kernel](./samples/getting_started/01-basic-loading-the-kernel.ipynb)
+- [Running AI prompts from file](./samples/getting_started/02-running-prompts-from-file.ipynb)
+- [Creating Prompt Functions at runtime (i.e. inline functions)](./samples/getting_started/03-prompt-function-inline.ipynb)
+- [Using Context Variables to Build a Chat Experience](./samples/getting_started/04-kernel-arguments-chat.ipynb)
+- [Introduction to planners](./samples/getting_started/05-using-the-planner.ipynb)
+- [Building Memory with Embeddings](./samples/getting_started/06-memory-and-embeddings.ipynb)
+- [Using Hugging Face for Plugins](./samples/getting_started/07-hugging-face-for-plugins.ipynb)
+- [Combining native functions and semantic functions](./samples/getting_started/08-native-function-inline.ipynb)
+- [Groundedness Checking with Semantic Kernel](./samples/getting_started/09-groundedness-checking.ipynb)
+- [Returning multiple results per prompt](./samples/getting_started/10-multiple-results-per-prompt.ipynb)
+- [Streaming completions with Semantic Kernel](./samples/getting_started/11-streaming-completions.ipynb)
 
 # SK Frequently Asked Questions
 

python/samples/concepts/README.md

@@ -0,0 +1,19 @@
+# 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`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/chat_completion_client_base.py) messaging capable service with models  |
+| 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 |
+| 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 |
+| 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) |
+| 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 |
+| 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  |

python/samples/concepts/functions/kernel_arguments.py

@@ -0,0 +1,72 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from __future__ import annotations
+
+import asyncio
+import datetime
+import locale
+from typing import Annotated
+
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+from semantic_kernel.functions.kernel_function_decorator import kernel_function
+from semantic_kernel.kernel import Kernel
+
+# This example shows how to use kernel arguments when invoking functions.
+
+
+class StaticTextPlugin:
+    """A plugin for generating static text."""
+
+    @kernel_function(name="uppercase", description="Convert text to uppercase")
+    def uppercase(
+        self, text: Annotated[str, "The input text"]
+    ) -> Annotated[str, "The output is the text in uppercase"]:
+        """Convert text to uppercase.
+
+        Args:
+            text (str): The text to convert to uppercase.
+
+        Returns:
+            str: The text in uppercase.
+        """
+        return text.upper()
+
+    @kernel_function(name="append_day", description="Append the day variable")
+    def append_day(
+        self, input: Annotated[str, "The input text"], day: Annotated[str, "The day to append"]
+    ) -> Annotated[str, "The output is the text with the day appended"]:
+        """Append the day variable.
+
+        Args:
+            input (str): The input text to append the day to.
+            day (str): The day to append.
+
+        Returns:
+            str: The text with the day appended.
+        """
+        return f"{input} {day}"
+
+
+def get_day_of_week_for_locale():
+    """Get the day of the week for the current locale."""
+    locale.setlocale(locale.LC_TIME, "")
+    return datetime.datetime.now().strftime("%A")
+
+
+async def main():
+    kernel = Kernel()
+
+    text_plugin = kernel.add_plugin(StaticTextPlugin(), "TextPlugin")
+    arguments = KernelArguments(input="Today is:", day=get_day_of_week_for_locale())
+
+    result = await kernel.invoke(text_plugin["append_day"], arguments)
+
+    # The result returned is of type FunctionResult. Printing the result calls the __str__ method.
+    print(result)
+
+    # Note: if you need access to the result metadata, you can do the following
+    # metadata = result.metadata
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/demos/booking_restaurant/README.md

@@ -0,0 +1,129 @@
+# Restaurant - Demo Application
+
+This sample provides a practical demonstration of how to leverage features from the [Semantic Kernel](https://learn.microsoft.com/en-us/semantic-kernel) to build a console application. Specifically, the application utilizes the [Business Schedule and Booking API](https://www.microsoft.com/en-us/microsoft-365/business/scheduling-and-booking-app) through Microsoft Graph to enable a Large Language Model (LLM) to book restaurant appointments efficiently. This guide will walk you through the necessary steps to integrate these technologies seamlessly.
+
+## Prerequisites
+
+- Python 3.10, 3.11, or 3.12.
+- [Microsoft 365 Business License](https://www.microsoft.com/en-us/microsoft-365/business/compare-all-microsoft-365-business-products) to use [Business Schedule and Booking API](https://www.microsoft.com/en-us/microsoft-365/business/scheduling-and-booking-app).
+- [Azure Entra Id](https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id) administrator account to register an application and set the necessary credentials and permissions.
+
+### Function Calling Enabled Models
+
+This sample uses function calling capable models and has been tested with the following models:
+
+| Model type      | Model name/id             |       Model version | Supported |
+| --------------- | ------------------------- | ------------------: | --------- |
+| Chat Completion | gpt-3.5-turbo             |                0125 | ✅        |
+| Chat Completion | gpt-3.5-turbo-1106        |                1106 | ✅        |
+| Chat Completion | gpt-3.5-turbo-0613        |                0613 | ✅        |
+| Chat Completion | gpt-3.5-turbo-0301        |                0301 | ❌        |
+| Chat Completion | gpt-3.5-turbo-16k         |                0613 | ✅        |
+| Chat Completion | gpt-4                     |                0613 | ✅        |
+| Chat Completion | gpt-4-0613                |                0613 | ✅        |
+| Chat Completion | gpt-4-0314                |                0314 | ❌        |
+| Chat Completion | gpt-4-turbo               |          2024-04-09 | ✅        |
+| Chat Completion | gpt-4-turbo-2024-04-09    |          2024-04-09 | ✅        |
+| Chat Completion | gpt-4-turbo-preview       |        0125-preview | ✅        |
+| Chat Completion | gpt-4-0125-preview        |        0125-preview | ✅        |
+| Chat Completion | gpt-4-vision-preview      | 1106-vision-preview | ✅        |
+| Chat Completion | gpt-4-1106-vision-preview | 1106-vision-preview | ✅        |
+
+ℹ️ OpenAI Models older than 0613 version do not support function calling.
+
+ℹ️ When using Azure OpenAI, ensure that the model name of your deployment matches any of the above supported models names.
+
+## Configuring the sample
+
+Please make sure your .env file contains the following:
+
+- "BOOKING_SAMPLE_CLIENT_ID"
+- "BOOKING_SAMPLE_TENANT_ID"
+- "BOOKING_SAMPLE_CLIENT_SECRET"
+
+### Create an App Registration in Azure Active Directory
+
+1. Go to the [Azure Portal](https://portal.azure.com/).
+2. Select the Azure Active Directory service.
+3. Select App registrations and click on New registration.
+4. Fill in the required fields and click on Register.
+5. Copy the Application **(client) Id** for later use.
+6. Save Directory **(tenant) Id** for later use..
+7. Click on Certificates & secrets and create a new client secret. (Any name and expiration date will work)
+8. Copy the **client secret** value for later use.
+9. Click on API permissions and add the following permissions:
+   - Microsoft Graph
+     - Application permissions
+       - BookingsAppointment.ReadWrite.All
+     - Delegated permissions
+       - OpenId permissions
+         - offline_access
+         - profile
+         - openid
+
+### Create Or Use a Booking Service and Business
+
+1. Go to the [Bookings Homepage](https://outlook.office.com/bookings) website.
+2. Create a new Booking Page and add a Service to the Booking (Skip if you don't ).
+3. Access [Graph Explorer](https://developer.microsoft.com/en-us/graph/graph-explorer)
+4. Run the following query to get the Booking Business Id:
+   ```http
+   GET https://graph.microsoft.com/v1.0/solutions/bookingBusinesses
+   ```
+5. Copy the **Booking Business Id** for later use.
+6. Run the following query and replace it with your **Booking Business Id** to get the Booking Service Id
+   ```http
+   GET https://graph.microsoft.com/v1.0/solutions/bookingBusinesses/{bookingBusiness-id}/services
+   ```
+7. Copy the **Booking Service Id** for later use.
+
+## Running the sample
+
+Either run the sample in an IDE, like VSCode, or via the command line.
+
+### Example of a conversation
+
+> **User** > Please book a restaurant for me.
+
+> **Assistant** > Sure, I can help you with that. Could you please provide me with the following information:
+>
+> 1. Name of the restaurant you would like to book.
+> 2. Date and time of the reservation.
+> 3. Number of people in your party.
+> 4. Your name.
+> 5. Your email address.
+> 6. Your phone number.
+>
+> Once I have all this information, I will proceed with the booking.
+
+> **User** > I would like to book a table at the restaurant "The French Laundry" on the 15th of March at 7:00 PM for 4 people. My name is John Doe, my email address is [email protected], and my phone number is 123-456-7890.
+
+> **System** > Do you want to book a table at The French Laundry on 15/03/2023 19:00:00 for 4 people?
+
+> **System** > Please confirm by typing 'yes' or 'no'.
+
+> **User** > yes
+
+> **Assistant** > Great! I have successfully booked a table for you at "The French Laundry" on the 15th of March at 7:00 PM for 4 people.
+>
+> Your reservation has been made under the name John Doe.
+>
+> If you have any further questions or need to make any changes to your reservation, please let me know.
+
+> **User** > List my current bookings
+
+> **Assistant** > You currently have one booking:
+>
+> - Restaurant: The French Laundry
+> - Date and Time: 15th of March at 7:00 PM
+> - Party Size: 4 people
+>
+> If you need any further assistance or if there's anything else I can help you with, please let me know.
+
+> **User** > Cancel my booking
+
+> **System** > `[Cancelling a reservation for 4 at The French Laundry on 2023-03-15 at 19:00:00]`
+
+> **Assistant** > I have successfully canceled your booking at "The French Laundry" on the 15th of March at 7:00 PM for 4 people.
+>
+> If you have any other questions or need further assistance, please let me know.

python/samples/kernel-syntax-examples/resources/bookings_plugin/bookings_plugin.py → python/samples/demos/booking_restaurant/bookings_plugin/bookings_plugin.py

@@ -63,6 +63,11 @@ async def book_table(
         Returns:
             str: The status of the booking.
         """
+        print(f"System > Do you want to book a table at {restaurant} on {date_time} for {party_size} people?")
+        print("System > Please confirm by typing 'yes' or 'no'.")
+        confirmation = input("User:> ")
+        if confirmation.lower() != "yes":
+            return "Booking aborted by the user."
         request_body = BookingAppointment(
             odata_type="#microsoft.graph.bookingAppointment",
             customer_time_zone=self.customer_timezone,
@@ -107,7 +112,7 @@ async def book_table(
             self.booking_business_id
         ).appointments.post(request_body)
 
-        return response.id
+        return f"Booking successful! Your reservation ID is {response.id}."
 
     @kernel_function(name="list_revervations", description="List all reservations")
     async def list_reservations(self) -> Annotated[str, "The list of reservations"]:
@@ -126,26 +131,18 @@ async def list_reservations(self) -> Annotated[str, "The list of reservations"]:
     async def cancel_reservation(
         self,
         reservation_id: Annotated[str, "The ID of the reservation"],
+        restaurant: Annotated[str, "The name of the restaurant"],
+        date: Annotated[str, "The date of the reservation"],
+        time: Annotated[str, "The time of the reservation"],
+        party_size: Annotated[int, "The number of people in the party"],
     ) -> Annotated[str, "The cancellation status of the reservation"]:
         """Cancel a reservation."""
 
-        # The graph API is throwing a 500 (instead of a 400), so commenting this out for now until we
-        # can understand how to get it working.
-        # Filed issue: https://github.com/microsoftgraph/msgraph-sdk-python/issues/659
-
-        # # First cancel the reservation
-        # request_body = CancelPostRequestBody(
-        #     comment="Your appointment has been successfully cancelled. Please call us again.",
-        # )
-
-        # await self.graph_client.solutions.booking_businesses.by_booking_business_id(
-        #     self.booking_business_id
-        # ).appointments.by_booking_appointment_id(reservation.id).cancel.post(request_body)
-
-        # # Then delete the reservation
-        # _ = (
-        #     await self.graph_client.solutions.booking_businesses.by_booking_business_id(self.booking_business_id)
-        #     .appointments.by_booking_appointment_id(reservation.id)
-        #     .delete()
-        # )
-        return "Reservation canceled!"
+        print(f"System > [Cancelling a reservation for {party_size} at {restaurant} on {date} at {time}]")
+
+        _ = (
+            await self.graph_client.solutions.booking_businesses.by_booking_business_id(self.booking_business_id)
+            .appointments.by_booking_appointment_id(reservation_id)
+            .delete()
+        )
+        return "Cancellation successful!"

python/samples/kernel-syntax-examples/restaurant_booking.py → python/samples/demos/booking_restaurant/restaurant_booking.py

@@ -3,9 +3,9 @@
 import asyncio
 
 from azure.identity import ClientSecretCredential
+from bookings_plugin.bookings_plugin import BookingsPlugin
 from dotenv import dotenv_values
 from msgraph import GraphServiceClient
-from resources.bookings_plugin.bookings_plugin import BookingsPlugin
 
 from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
 from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.open_ai_prompt_execution_settings import (
@@ -18,13 +18,6 @@
 from semantic_kernel.kernel import Kernel
 from semantic_kernel.utils.settings import booking_sample_settings_from_dot_env_as_dict, openai_settings_from_dot_env
 
-# To be able to run this sample, you must do the following:
-# 1. Create an Microsoft Entra App ID and Client Secret in Azure Portal
-# 2. Add the client ID, tenant ID, and client secret to a .env file in the root of the project
-#   using the following format: BOOKING_SAMPLE_CLIENT_ID="", BOOKING_SAMPLE_TENANT_ID="",
-#   BOOKING_SAMPLE_CLIENT_SECRET="".
-# 3. Create a booking business ID and service ID and give the app permissions based on your App Id and secret.
-
 kernel = Kernel()
 
 service_id = "open_ai"