Convert example code from Python to C# and Semantic Kernel

02-explore-agentic-frameworks/README.md

@@ -62,50 +62,67 @@ Frameworks like LangChain and Microsoft Semantic Kernel offer pre-built componen
 
 **Example code**. Let's look at an example of how you can use a pre-built parser to extract information from user input:
 
-```python
-import os
-from langchain.output_parsers import PydanticOutputParser
-from langchain.prompts import PromptTemplate
-from langchain_openai import AzureChatOpenAI
-from pydantic import BaseModel, Field
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-
-# Define a Pydantic model for structured data
-class FlightBooking(BaseModel):
-    origin: str = Field(description="Departure city")
-    destination: str = Field(description="Arrival city")
-    date: str = Field(description="Flight date")
-
-# Initialize parser
-parser = PydanticOutputParser(pydantic_object=FlightBooking)
-
-# Define the prompt template
-prompt = PromptTemplate(
-    template="Extract structured flight details from the following text:\n{text}\n{format_instructions}",
-    input_variables=["text"],
-    partial_variables={"format_instructions": parser.get_format_instructions()},
-)
+```csharp
 
-# Initialize Azure OpenAI LLM
-llm = AzureChatOpenAI(
-    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
-    openai_api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
-    deployment_name=os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME"),
-    openai_api_key=os.getenv("AZURE_OPENAI_API_KEY"),
-    temperature=0
-)
+// Semantic Kernel example
+
+ChatHistory chatHistory = [];
+chatHistory.AddUserMessage("I'd like to go To New York");
+
+// Define a plugin that contains the function to book travel
+public class BookTravelPlugin(
+    IPizzaService pizzaService,
+    IUserContext userContext,
+    IPaymentService paymentService)
+{
+
+    [KernelFunction("book_flight")]
+    [Description("Book travel given location and date")]
+    public async Task<Booking> BookFlight(
+        DateTime date,
+        string location,
+    )
+    {
+        // book travel given date,location
+    }
+}
+
+IKernelBuilder kernelBuilder = new KernelBuilder();
+kernelBuilder..AddAzureOpenAIChatCompletion(
+    deploymentName: "NAME_OF_YOUR_DEPLOYMENT",
+    apiKey: "YOUR_API_KEY",
+    endpoint: "YOUR_AZURE_ENDPOINT"
+);
+kernelBuilder.Plugins.AddFromType<BookTravelPlugin>("BookTravel");
+Kernel kernel = kernelBuilder.Build();
+
+/*
+Behind the scenes, i.e recognizes the tool to call, what arguments it already has (location) and what it needs (date)
+{
+
+"tool_calls": [
+    {
+        "id": "call_abc123",
+        "type": "function",
+        "function": {
+            "name": "BookTravelPlugin-book_flight",
+            "arguments": "{\n\"location\": \"New York\",\n\"date\": \"15th July\"\n}"
+        }
+    }
+]
+*/
+
+ChatResponse response = await chatCompletion.GetChatMessageContentAsync(
+    chatHistory,
+    executionSettings: openAIPromptExecutionSettings,
+    kernel: kernel)
 
-# Chain together the prompt, model, and parser
-chain = prompt | llm | parser
 
-# Invoke the chain
-parsed_data = chain.invoke({"text": user_input})
+Console.WriteLine(response);
+chatHistory.AddAssistantMessage(response);
 
-print(parsed_data)
-# Output: origin='New York' destination='London' date='July 15th'
+// AI Response: "Before I can book your flight, I need to know your departure date. When are you planning to travel?"
+// I.e above it figures out the tool to call, what arguments it already has (location) and what it needs (date) from the user input, at this point it ends up asking the user for the missing information
 ```
 
 What you can see from this example is how you can leverage a pre-built parser to extract key information from user input, such as the origin, destination, and date of a flight booking request. This modular approach allows you to focus on the high-level logic.