docs: add PZERO OpenAI-compatible endpoint sample and documentation

dotnet/docs/OPENAI-CONNECTOR-MIGRATION.md

@@ -73,6 +73,22 @@ We have the two only specific cases where we attempted to auto-correct the endpo
    + http://any-host-and-port/v1
    ```
 
+### 5.1 OpenAI-Compatible Endpoints Example (e.g., PZERO)
+
+When connecting to third-party OpenAI-compatible endpoints such as PZERO (`https://api.pzero.studio/v1`), provide the `/v1` root endpoint, API key, and model ID:
+
+```csharp
+using Microsoft.SemanticKernel;
+
+var builder = Kernel.CreateBuilder();
+builder.AddOpenAIChatCompletion(
+    modelId: "deepseek-v4-flash",
+    endpoint: new Uri("https://api.pzero.studio/v1"),
+    apiKey: Environment.GetEnvironmentVariable("PZERO_API_KEY")!
+);
+var kernel = builder.Build();
+```
+
 ## 6. SemanticKernel MetaPackage
 
 To be retro compatible with the new OpenAI and AzureOpenAI Connectors, our `Microsoft.SemanticKernel` meta package changed its dependency to use the new `Microsoft.SemanticKernel.Connectors.AzureOpenAI` package that depends on the `Microsoft.SemanticKernel.Connectors.OpenAI` package. This way if you are using the metapackage, no change is needed to get access to `Azure` related types.
@@ -181,15 +197,14 @@ The type also changed from `CompletionsUsage` to `ChatTokenUsage`.
 ```diff
 - Before
 - var usage = FunctionResult.Metadata?["Usage"] as CompletionsUsage;
-- var completionTokesn = usage?.CompletionTokens ?? 0;
+- var completionTokens = usage?.CompletionTokens ?? 0;
 - var promptTokens = usage?.PromptTokens ?? 0;
 
 + After
 + var usage = FunctionResult.Metadata?["Usage"] as ChatTokenUsage;
 + var promptTokens = usage?.InputTokens ?? 0;
-+ var completionTokens = completionTokens: usage?.OutputTokens ?? 0;
-
-totalTokens: usage?.TotalTokens ?? 0;
++ var completionTokens = usage?.OutputTokens ?? 0;
++ var totalTokens = usage?.TotalTokens ?? 0;
 ```
 
 #### 9.9 OpenAIClient

dotnet/samples/Concepts/ChatCompletion/PZero_ChatCompletion.cs

@@ -0,0 +1,92 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.ChatCompletion;
+using Microsoft.SemanticKernel.Connectors.OpenAI;
+
+namespace ChatCompletion;
+
+/// <summary>
+/// This example shows how to use the OpenAI connector with PZERO's OpenAI-compatible endpoint.
+/// <list type="number">
+/// <item>Get a key from https://pzero.studio/agents</item>
+/// <item>Set the environment variable PZERO_API_KEY with your key</item>
+/// <item>Configure the endpoint to https://api.pzero.studio/v1</item>
+/// <item>Run the example</item>
+/// </list>
+/// </summary>
+public class PZero_ChatCompletion(ITestOutputHelper output) : BaseTest(output)
+{
+    /// <summary>
+    /// This example shows how to configure PZERO OpenAI-compatible endpoint with Kernel InvokeAsync.
+    /// </summary>
+    [Fact]
+    public async Task UsingKernelWithPZero()
+    {
+        Console.WriteLine($"======== PZERO - Chat Completion - {nameof(UsingKernelWithPZero)} ========");
+
+        var apiKey = Environment.GetEnvironmentVariable("PZERO_API_KEY");
+        if (string.IsNullOrEmpty(apiKey))
+        {
+            Console.WriteLine("PZERO_API_KEY environment variable is not set. Skipping execution.");
+            return;
+        }
+
+        var modelId = "deepseek-v4-flash";
+        var endpoint = new Uri("https://api.pzero.studio/v1");
+
+        var kernel = Kernel.CreateBuilder()
+            .AddOpenAIChatCompletion(
+                modelId: modelId,
+                endpoint: endpoint,
+                apiKey: apiKey)
+            .Build();
+
+        var prompt = @"Rewrite the text between triple backticks into a business email. Use a professional tone, be clear and concise.
+                   Sign the email as AI Assistant.
+
+                   Text: ```{{$input}}```";
+
+        var mailFunction = kernel.CreateFunctionFromPrompt(prompt, new OpenAIPromptExecutionSettings
+        {
+            TopP = 0.5,
+            MaxTokens = 1000,
+        });
+
+        var response = await kernel.InvokeAsync(mailFunction, new() { ["input"] = "Tell David that I will complete the report by Friday." });
+        Console.WriteLine(response);
+    }
+
+    /// <summary>
+    /// Sample showing how to use <see cref="IChatCompletionService"/> directly with a <see cref="ChatHistory"/> against PZERO.
+    /// </summary>
+    [Fact]
+    public async Task UsingServiceNonStreamingWithPZero()
+    {
+        Console.WriteLine($"======== PZERO - Chat Completion - {nameof(UsingServiceNonStreamingWithPZero)} ========");
+
+        var apiKey = Environment.GetEnvironmentVariable("PZERO_API_KEY");
+        if (string.IsNullOrEmpty(apiKey))
+        {
+            Console.WriteLine("PZERO_API_KEY environment variable is not set. Skipping execution.");
+            return;
+        }
+
+        var modelId = "deepseek-v4-flash";
+        var endpoint = new Uri("https://api.pzero.studio/v1");
+
+        OpenAIChatCompletionService chatService = new(modelId: modelId, endpoint: endpoint, apiKey: apiKey);
+
+        Console.WriteLine("Chat content:");
+        Console.WriteLine("------------------------");
+
+        var chatHistory = new ChatHistory("You are a helpful assistant.");
+
+        chatHistory.AddUserMessage("Hello! Can you summarize the purpose of Semantic Kernel in one sentence?");
+        this.OutputLastMessage(chatHistory);
+
+        var reply = await chatService.GetChatMessageContentAsync(chatHistory);
+        chatHistory.Add(reply);
+        this.OutputLastMessage(chatHistory);
+    }
+}

dotnet/samples/Concepts/README.md

@@ -97,6 +97,7 @@ dotnet test -l "console;verbosity=detailed" --filter "FullyQualifiedName=ChatCom
 - [OpenAI_RepeatedFunctionCalling](https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/ChatCompletion/OpenAI_RepeatedFunctionCalling.cs)
 - [OpenAI_StructuredOutputs](https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/ChatCompletion/OpenAI_StructuredOutputs.cs)
 - [OpenAI_UsingLogitBias](https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/ChatCompletion/OpenAI_UsingLogitBias.cs)
+- [PZero_ChatCompletion](https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/ChatCompletion/PZero_ChatCompletion.cs)
 
 ### DependencyInjection - Examples on using `DI Container`
 

python/samples/concepts/chat_completion/pzero_chat_completion.py

@@ -0,0 +1,59 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+
+from openai import AsyncOpenAI
+
+from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+from semantic_kernel.kernel import Kernel
+
+# This concept sample shows how to use the OpenAI connector with PZERO's
+# OpenAI-compatible endpoint: https://api.pzero.studio/v1
+# Get an API key from https://pzero.studio/agents and set PZERO_API_KEY.
+
+system_message = """
+You are a helpful and concise AI assistant.
+"""
+
+kernel = Kernel()
+
+service_id = "pzero-deepseek"
+
+api_key = os.environ.get("PZERO_API_KEY", "your-pzero-api-key")
+endpoint = "https://api.pzero.studio/v1"
+model_id = "deepseek-v4-flash"
+
+open_ai_client: AsyncOpenAI = AsyncOpenAI(
+    api_key=api_key,
+    base_url=endpoint,
+)
+kernel.add_service(OpenAIChatCompletion(service_id=service_id, ai_model_id=model_id, async_client=open_ai_client))
+
+settings = kernel.get_prompt_execution_settings_from_service_id(service_id)
+settings.max_tokens = 1000
+settings.temperature = 0.7
+
+chat_function = kernel.add_function(
+    plugin_name="ChatBot",
+    function_name="Chat",
+    prompt="{{$chat_history}}{{$user_input}}",
+    template_format="semantic-kernel",
+    prompt_execution_settings=settings,
+)
+
+
+async def main() -> None:
+    chat_history = ChatHistory(system_message=system_message)
+    user_message = "What is Semantic Kernel in one sentence?"
+    chat_history.add_user_message(user_message)
+
+    answer = await kernel.invoke(chat_function, KernelArguments(user_input=user_message, chat_history=chat_history))
+    print(f"User:> {user_message}")
+    print(f"Assistant:> {answer}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())