.NET: Fix IDE0039 by using local functions in samples

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step01_BasicTextRAG/Program.cs

@@ -37,7 +37,7 @@
 await textSearchStore.UpsertDocumentsAsync(GetSampleDocuments());
 
 // Create an adapter function that the TextSearchProvider can use to run searches against the TextSearchStore.
-Func<string, CancellationToken, Task<IEnumerable<TextSearchProvider.TextSearchResult>>> SearchAdapter = async (text, ct) =>
+async Task<IEnumerable<TextSearchProvider.TextSearchResult>> SearchAdapterAsync(string text, CancellationToken ct)
 {
     // Here we are limiting the search results to the single top result to demonstrate that we are accurately matching
     // specific search results for each question, but in a real world case, more results should be used.
@@ -49,7 +49,7 @@
         Text = r.Text ?? string.Empty,
         RawRepresentation = r
     });
-};
+}
 
 // Configure the options for the TextSearchProvider.
 TextSearchProviderOptions textSearchOptions = new()
@@ -63,7 +63,7 @@
     .AsAIAgent(new ChatClientAgentOptions
     {
         ChatOptions = new() { ModelId = deploymentName, Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available." },
-        AIContextProviders = [new TextSearchProvider(SearchAdapter, textSearchOptions)],
+        AIContextProviders = [new TextSearchProvider(SearchAdapterAsync, textSearchOptions)],
         // Since we are using ChatCompletion which stores chat history locally, we can also add a message filter
         // that removes messages produced by the TextSearchProvider before they are added to the chat history, so that
         // we don't bloat chat history with all the search result messages.

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/Program.cs

@@ -40,7 +40,7 @@
 await UploadDataFromMarkdown(afMigrationUrl, "Semantic Kernel to Microsoft Agent Framework Migration Guide", documentationCollection, 2000, 200);
 
 // Create an adapter function that the TextSearchProvider can use to run searches against the collection.
-Func<string, CancellationToken, Task<IEnumerable<TextSearchProvider.TextSearchResult>>> SearchAdapter = async (text, ct) =>
+async Task<IEnumerable<TextSearchProvider.TextSearchResult>> SearchAdapterAsync(string text, CancellationToken ct)
 {
     List<TextSearchProvider.TextSearchResult> results = [];
     await foreach (var result in documentationCollection.SearchAsync(text, 5, cancellationToken: ct))
@@ -54,7 +54,7 @@
         });
     }
     return results;
-};
+}
 
 // Configure the options for the TextSearchProvider.
 TextSearchProviderOptions textSearchOptions = new()
@@ -72,7 +72,7 @@
     .AsAIAgent(new ChatClientAgentOptions
     {
         ChatOptions = new() { ModelId = deploymentName, Instructions = "You are a helpful support specialist for the Microsoft Agent Framework. Answer questions using the provided context and cite the source document when available. Keep responses brief." },
-        AIContextProviders = [new TextSearchProvider(SearchAdapter, textSearchOptions)],
+        AIContextProviders = [new TextSearchProvider(SearchAdapterAsync, textSearchOptions)],
         // Configure a filter on the InMemoryChatHistoryProvider so that we don't persist the messages produced by the TextSearchProvider in chat history.
         // The default is to persist all messages except those that came from chat history in the first place.
         // You may choose to persist the TextSearchProvider messages, if you want the search output to be provided to the model in future interactions as well.

dotnet/samples/02-agents/Agents/Agent_Step17_AdditionalAIContext/Program.cs

@@ -23,7 +23,7 @@
 var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // A sample function to load the next three calendar events for the user.
-Func<Task<string[]>> loadNextThreeCalendarEvents = async () =>
+async Task<string[]> LoadNextThreeCalendarEventsAsync()
 {
     // In a real implementation, this method would connect to a calendar service
     return
@@ -32,7 +32,7 @@
         "Team meeting today at 17:00",
         "Birthday party today at 20:00"
     ];
-};
+}
 
 // Create an agent with an AI context provider attached that aggregates two other providers.
 // You must dissable client side conversation storage for clients that support it:
@@ -64,7 +64,7 @@ You remind users of upcoming calendar events when the user interacts with you.
         // The agent will call each provider in sequence, accumulating context from each.
         AIContextProviders = [
             new TodoListAIContextProvider(),
-            new CalendarSearchAIContextProvider(loadNextThreeCalendarEvents)
+            new CalendarSearchAIContextProvider(LoadNextThreeCalendarEventsAsync)
         ],
     });