use DefaultAzureCredential instead of AzureCliCredential (#3860)

dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step01_BasicTextRAG/Program.cs

@@ -18,9 +18,12 @@ var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? th
 var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
 var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
 
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AzureOpenAIClient azureOpenAIClient = new(
     new Uri(endpoint),
-    new AzureCliCredential());
+    new DefaultAzureCredential());
 
 // Create an In-Memory vector store that uses the Azure OpenAI embedding model to generate embeddings.
 VectorStore vectorStore = new InMemoryVectorStore(new()

dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/Program.cs

@@ -19,9 +19,12 @@ var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_E
 var afOverviewUrl = "https://github.com/MicrosoftDocs/semantic-kernel-docs/blob/main/agent-framework/overview/agent-framework-overview.md";
 var afMigrationUrl = "https://raw.githubusercontent.com/MicrosoftDocs/semantic-kernel-docs/refs/heads/main/agent-framework/migration-guide/from-semantic-kernel/index.md";
 
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AzureOpenAIClient azureOpenAIClient = new(
     new Uri(endpoint),
-    new AzureCliCredential());
+    new DefaultAzureCredential());
 
 // Create a Qdrant vector store that uses the Azure OpenAI embedding model to generate embeddings.
 QdrantClient client = new("localhost");

dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step03_CustomRAGDataSource/Program.cs

@@ -22,9 +22,12 @@ TextSearchProviderOptions textSearchOptions = new()
     RecentMessageMemoryLimit = 6,
 };
 
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIAgent agent = new AzureOpenAIClient(
     new Uri(endpoint),
-    new AzureCliCredential())
+    new DefaultAzureCredential())
     .GetChatClient(deploymentName)
     .AsAIAgent(new ChatClientAgentOptions
     {

dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/Program.cs

@@ -15,9 +15,12 @@ var endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOIN
 var deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
 
 // Create an AI Project client and get an OpenAI client that works with the foundry service.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIProjectClient aiProjectClient = new(
     new Uri(endpoint),
-    new AzureCliCredential());
+    new DefaultAzureCredential());
 OpenAIClient openAIClient = aiProjectClient.GetProjectOpenAIClient();
 
 // Upload the file that contains the data to be used for RAG to the Foundry service.