Refactor token provider usage for Azure services

Replaces direct calls to config.get_access_token() with a dedicated ad_token_provider method in both settings and reasoning agent classes. This improves consistency and encapsulation of token retrieval logic for Azure Cognitive Services and management APIs.

Author: Fr4nc3

src/backend/v3/common/services/foundry_service.py

@@ -2,7 +2,7 @@
 import re
 from typing import Any, Dict, List
 
-#from git import List
+# from git import List
 import aiohttp
 from azure.ai.projects.aio import AIProjectClient
 from common.config.app_config import config
@@ -44,7 +44,6 @@ async def get_connection(self, name: str) -> Dict[str, Any]:
     # -----------------------
     # Model validation methods
     # -----------------------
-
     async def list_model_deployments(self) -> List[Dict[str, Any]]:
         """
         List all model deployments in the Azure AI project using the REST API.
@@ -55,19 +54,23 @@ async def list_model_deployments(self) -> List[Dict[str, Any]]:
 
         try:
             # Get Azure Management API token (not Cognitive Services token)
-            token = await config.get_access_token()
+            credential = config.get_azure_credentials()
+            token = credential.get_token(config.AZURE_MANAGEMENT_SCOPE)
+
 
             # Extract Azure OpenAI resource name from endpoint URL
             openai_endpoint = config.AZURE_OPENAI_ENDPOINT
             # Extract resource name from URL like "https://aisa-macae-d3x6aoi7uldi.openai.azure.com/"
-            match = re.search(r'https://([^.]+)\.openai\.azure\.com', openai_endpoint)
+            match = re.search(r"https://([^.]+)\.openai\.azure\.com", openai_endpoint)
             if not match:
-                self.logger.error(f"Could not extract resource name from endpoint: {openai_endpoint}")
+                self.logger.error(
+                    f"Could not extract resource name from endpoint: {openai_endpoint}"
+                )
                 return []
-            
+
             openai_resource_name = match.group(1)
             self.logger.info(f"Using Azure OpenAI resource: {openai_resource_name}")
-            
+
             # Query Azure OpenAI resource deployments
             url = (
                 f"https://management.azure.com/subscriptions/{self.subscription_id}/"

src/backend/v3/config/settings.py

@@ -34,16 +34,20 @@ def __init__(self):
         # Create credential
         self.credential = config.get_azure_credentials()
 
+    def ad_token_provider(self) -> str:
+        token = self.credential.get_token(config.AZURE_COGNITIVE_SERVICES)
+        return token.token
+
     async def create_chat_completion_service(self, use_reasoning_model: bool=False):
         """Create Azure Chat Completion service."""
         model_name = (
             self.reasoning_model if use_reasoning_model else self.standard_model
         )
-
+        # Create Azure Chat Completion service
         return AzureChatCompletion(
             deployment_name=model_name,
             endpoint=self.endpoint,
-            ad_token_provider= await config.get_access_token(),
+            ad_token_provider=self.ad_token_provider,
         )
 
     def create_execution_settings(self):

src/backend/v3/magentic_agents/reasoning_agent.py

@@ -18,31 +18,39 @@ class ReasoningAgentTemplate(MCPEnabledBase):
     No Azure AI Agents client is needed here. We only need a token provider for SK.
     """
 
-    def __init__(self, agent_name: str, 
-                 agent_description: str, 
-                 agent_instructions: str,
-                 model_deployment_name: str,
-                 azure_openai_endpoint: str,
-                 search_config: SearchConfig | None = None,
-                 mcp_config: MCPConfig | None = None) -> None:
+    def __init__(
+        self,
+        agent_name: str,
+        agent_description: str,
+        agent_instructions: str,
+        model_deployment_name: str,
+        azure_openai_endpoint: str,
+        search_config: SearchConfig | None = None,
+        mcp_config: MCPConfig | None = None,
+    ) -> None:
         super().__init__(mcp=mcp_config)
         self.agent_name = agent_name
         self.agent_description = agent_description
         self.agent_instructions = agent_instructions
         self._model_deployment_name = model_deployment_name
         self._openai_endpoint = azure_openai_endpoint
-        self.search_config = search_config 
+        self.search_config = search_config
         self.reasoning_search: ReasoningSearch | None = None
         self.logger = logging.getLogger(__name__)
 
+    def ad_token_provider(self) -> str:
+        credential = config.get_azure_credentials()
+        token = credential.get_token(config.AZURE_COGNITIVE_SERVICES)
+        return token.token
+
     async def _after_open(self) -> None:
         self.kernel = Kernel()
 
-
+        # Add Azure OpenAI Chat Completion service
         chat = AzureChatCompletion(
             deployment_name=self._model_deployment_name,
             endpoint=self._openai_endpoint,
-            ad_token_provider= await config.get_access_token()
+            ad_token_provider=self.ad_token_provider,
         )
         self.kernel.add_service(chat)
 
@@ -63,34 +71,36 @@ async def _after_open(self) -> None:
             kernel=self.kernel,
             name=self.agent_name,
             description=self.agent_description,
-            instructions=self.agent_instructions
+            instructions=self.agent_instructions,
         )
-    
+
     async def invoke(self, message: str):
         """Invoke the agent with a message."""
         if not self._agent:
             raise RuntimeError("Agent not initialized. Call open() first.")
-        
+
         async for response in self._agent.invoke(message):
             yield response
-        
+
+
 # Backward‑compatible factory
 async def create_reasoning_agent(
-            agent_name: str, 
-            agent_description: str, 
-            agent_instructions: str,
-            model_deployment_name: str,
-            azure_openai_endpoint: str,
-            search_config: SearchConfig | None = None,
-            mcp_config: MCPConfig | None = None) -> ReasoningAgentTemplate:
+    agent_name: str,
+    agent_description: str,
+    agent_instructions: str,
+    model_deployment_name: str,
+    azure_openai_endpoint: str,
+    search_config: SearchConfig | None = None,
+    mcp_config: MCPConfig | None = None,
+) -> ReasoningAgentTemplate:
     agent = ReasoningAgentTemplate(
-        agent_name=agent_name, 
-        agent_description=agent_description, 
+        agent_name=agent_name,
+        agent_description=agent_description,
         agent_instructions=agent_instructions,
         model_deployment_name=model_deployment_name,
         azure_openai_endpoint=azure_openai_endpoint,
-        search_config= search_config,
-        mcp_config=mcp_config
+        search_config=search_config,
+        mcp_config=mcp_config,
     )
     await agent.open()
     return agent