[Draft] Feature/azd semantickernel

infra/main.bicep

@@ -63,7 +63,7 @@ var modelVersion = '2024-08-06'
 var aiServicesName = '${prefix}-aiservices'
 var deploymentType  = 'GlobalStandard'
 var gptModelVersion = 'gpt-4o'
-var appVersion = 'latest'
+var appVersion = 'fnd01'
 var resgistryName = 'biabcontainerreg'
 var dockerRegistryUrl = 'https://${resgistryName}.azurecr.io'
 
@@ -196,6 +196,7 @@ resource cosmos 'Microsoft.DocumentDB/databaseAccounts@2024-05-15' = {
         locationName: location
       }
     ]
+    disableLocalAuth: true
     capabilities: [ { name: 'EnableServerless' } ]
   }
 

src/backend/app_config.py

@@ -220,30 +220,41 @@ async def create_azure_ai_agent(
             if client is None:
                 client = self.get_ai_project_client()
 
-            # First try to get an existing agent with this name as assistant_id
-            try:
-
-                existing_definition = await client.agents.get_agent(agent_name)
-                # Create the agent instance directly with project_client and existing definition
-                agent = AzureAIAgent(
-                    client=client,
-                    definition=existing_definition,
-                    plugins=tools,
-                )
-
-                return agent
-            except Exception as e:
-                # The Azure AI Projects SDK throws an exception when the agent doesn't exist
-                # (not returning None), so we catch it and proceed to create a new agent
-                if "ResourceNotFound" in str(e) or "404" in str(e):
-                    logging.info(
-                        f"Agent with ID {agent_name} not found. Will create a new one."
-                    )
-                else:
-                    # Log unexpected errors but still try to create a new agent
-                    logging.warning(
-                        f"Unexpected error while retrieving agent {agent_name}: {str(e)}. Attempting to create new agent."
-                    )
+            # # ToDo: This is the fixed code but commenting it out as agent clean up is no happening yet
+            #  # and there are multiple versions of agents due to testing
+            # # First try to get an existing agent with this name as assistant_id
+            # try:
+            #     agent_id = None
+            #     agent_list = await client.agents.list_agents()
+            #     for agent in agent_list.data:
+            #         if agent.name == agent_name:
+            #             agent_id = agent.id
+            #             break
+            #             # If the agent already exists, we can use it directly
+            #             # Get the existing agent definition
+            #     existing_definition = await client.agents.get_agent(agent_id)
+            #     # Create the agent instance directly with project_client and existing definition
+            #     agent = AzureAIAgent(
+            #         client=client,
+            #         definition=existing_definition,
+            #         plugins=tools,
+            #     )
+
+            #     client.agents.list_agents()
+
+            #     return agent
+            # except Exception as e:
+            #     # The Azure AI Projects SDK throws an exception when the agent doesn't exist
+            #     # (not returning None), so we catch it and proceed to create a new agent
+            #     if "ResourceNotFound" in str(e) or "404" in str(e):
+            #         logging.info(
+            #             f"Agent with ID {agent_name} not found. Will create a new one."
+            #         )
+            #     else:
+            #         # Log unexpected errors but still try to create a new agent
+            #         logging.warning(
+            #             f"Unexpected error while retrieving agent {agent_name}: {str(e)}. Attempting to create new agent."
+            #         )
 
             # Create the agent using the project client with the agent_name as both name and assistantId
             agent_definition = await client.agents.create_agent(

src/backend/app_kernel.py

@@ -40,16 +40,21 @@
 from event_utils import track_event_if_configured
 from models.messages_kernel import AgentType
 from kernel_agents.agent_factory import AgentFactory
+from app_config import config
 
 # # Check if the Application Insights Instrumentation Key is set in the environment variables
-# instrumentation_key = os.getenv("APPLICATIONINSIGHTS_INSTRUMENTATION_KEY")
-# if instrumentation_key:
-#     # Configure Application Insights if the Instrumentation Key is found
-#     configure_azure_monitor(connection_string=instrumentation_key)
-#     logging.info("Application Insights configured with the provided Instrumentation Key")
-# else:
-#     # Log a warning if the Instrumentation Key is not found
-#     logging.warning("No Application Insights Instrumentation Key found. Skipping configuration")
+instrumentation_key = os.getenv("APPLICATIONINSIGHTS_INSTRUMENTATION_KEY")
+if instrumentation_key:
+    # Configure Application Insights if the Instrumentation Key is found
+    configure_azure_monitor(connection_string=instrumentation_key)
+    logging.info(
+        "Application Insights configured with the provided Instrumentation Key"
+    )
+else:
+    # Log a warning if the Instrumentation Key is not found
+    logging.warning(
+        "No Application Insights Instrumentation Key found. Skipping configuration"
+    )
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -61,9 +66,9 @@
 logging.getLogger("azure.identity.aio._internal").setLevel(logging.WARNING)
 
 # # Suppress info logs from OpenTelemetry exporter
-# logging.getLogger("azure.monitor.opentelemetry.exporter.export._base").setLevel(
-#     logging.WARNING
-# )
+logging.getLogger("azure.monitor.opentelemetry.exporter.export._base").setLevel(
+    logging.WARNING
+)
 
 # Initialize the FastAPI app
 app = FastAPI()
@@ -124,10 +129,17 @@ async def input_task_endpoint(input_task: InputTask, request: Request):
         kernel, memory_store = await initialize_runtime_and_context(
             input_task.session_id, user_id
         )
+        client = None
+        try:
+            client = config.get_ai_project_client()
+        except Exception as client_exc:
+            logging.error(f"Error creating AIProjectClient: {client_exc}")
+
         agents = await AgentFactory.create_all_agents(
             session_id=input_task.session_id,
             user_id=user_id,
             memory_store=memory_store,
+            client=client,
         )
 
         group_chat_manager = agents[AgentType.GROUP_CHAT_MANAGER.value]
@@ -161,7 +173,11 @@ async def input_task_endpoint(input_task: InputTask, request: Request):
                 "description": input_task.description,
             },
         )
-
+        if client:
+            try:
+                client.close()
+            except Exception as e:
+                logging.error(f"Error sending to AIProjectClient: {e}")
         return {
             "status": f"Plan created with ID: {plan.id}",
             "session_id": input_task.session_id,
@@ -249,12 +265,31 @@ async def human_feedback_endpoint(human_feedback: HumanFeedback, request: Reques
     kernel, memory_store = await initialize_runtime_and_context(
         human_feedback.session_id, user_id
     )
-    agents = await AgentFactory.create_all_agents(
-        session_id=human_feedback.session_id, user_id=user_id, memory_store=memory_store
+
+    client = None
+    try:
+        client = config.get_ai_project_client()
+    except Exception as client_exc:
+        logging.error(f"Error creating AIProjectClient: {client_exc}")
+
+    human_agent = await AgentFactory.create_agent(
+        agent_type=AgentType.HUMAN,
+        session_id=human_feedback.session_id,
+        user_id=user_id,
+        memory_store=memory_store,
+        client=client,
     )
 
-    # Send the feedback to the human agent
-    human_agent = agents[AgentType.HUMAN.value]
+    if human_agent is None:
+        track_event_if_configured(
+            "AgentNotFound",
+            {
+                "status": "Agent not found",
+                "session_id": human_feedback.session_id,
+                "step_id": human_feedback.step_id,
+            },
+        )
+        raise HTTPException(status_code=404, detail="Agent not found")
 
     # Use the human agent to handle the feedback
     await human_agent.handle_human_feedback(human_feedback=human_feedback)
@@ -267,7 +302,11 @@ async def human_feedback_endpoint(human_feedback: HumanFeedback, request: Reques
             "step_id": human_feedback.step_id,
         },
     )
-
+    if client:
+        try:
+            client.close()
+        except Exception as e:
+            logging.error(f"Error sending to AIProjectClient: {e}")
     return {
         "status": "Feedback received",
         "session_id": human_feedback.session_id,
@@ -333,14 +372,30 @@ async def human_clarification_endpoint(
     kernel, memory_store = await initialize_runtime_and_context(
         human_clarification.session_id, user_id
     )
-    agents = await AgentFactory.create_all_agents(
+    client = None
+    try:
+        client = config.get_ai_project_client()
+    except Exception as client_exc:
+        logging.error(f"Error creating AIProjectClient: {client_exc}")
+
+    human_agent = await AgentFactory.create_agent(
+        agent_type=AgentType.HUMAN,
         session_id=human_clarification.session_id,
         user_id=user_id,
         memory_store=memory_store,
+        client=client,
     )
 
-    # Send the feedback to the human agent
-    human_agent = agents[AgentType.HUMAN.value]
+    if human_agent is None:
+        track_event_if_configured(
+            "AgentNotFound",
+            {
+                "status": "Agent not found",
+                "session_id": human_clarification.session_id,
+                "step_id": human_clarification.step_id,
+            },
+        )
+        raise HTTPException(status_code=404, detail="Agent not found")
 
     # Use the human agent to handle the feedback
     await human_agent.handle_human_clarification(
@@ -354,7 +409,11 @@ async def human_clarification_endpoint(
             "session_id": human_clarification.session_id,
         },
     )
-
+    if client:
+        try:
+            client.close()
+        except Exception as e:
+            logging.error(f"Error sending to AIProjectClient: {e}")
     return {
         "status": "Clarification received",
         "session_id": human_clarification.session_id,
@@ -427,17 +486,28 @@ async def approve_step_endpoint(
     kernel, memory_store = await initialize_runtime_and_context(
         human_feedback.session_id, user_id
     )
+    client = None
+    try:
+        client = config.get_ai_project_client()
+    except Exception as client_exc:
+        logging.error(f"Error creating AIProjectClient: {client_exc}")
     agents = await AgentFactory.create_all_agents(
         session_id=human_feedback.session_id,
         user_id=user_id,
         memory_store=memory_store,
+        client=client,
     )
 
     # Send the approval to the group chat manager
     group_chat_manager = agents[AgentType.GROUP_CHAT_MANAGER.value]
 
     await group_chat_manager.handle_human_feedback(human_feedback)
 
+    if client:
+        try:
+            client.close()
+        except Exception as e:
+            logging.error(f"Error sending to AIProjectClient: {e}")
     # Return a status message
     if human_feedback.step_id:
         track_event_if_configured(

src/backend/event_utils.py

@@ -5,10 +5,10 @@
 
 def track_event_if_configured(event_name: str, event_data: dict):
     """Track an event if Application Insights is configured.
-    
+
     This function safely wraps the Azure Monitor track_event function
     to handle potential errors with the ProxyLogger.
-    
+
     Args:
         event_name: The name of the event to track
         event_data: Dictionary of event data/dimensions
@@ -17,8 +17,10 @@ def track_event_if_configured(event_name: str, event_data: dict):
         instrumentation_key = os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING")
         if instrumentation_key:
             track_event(event_name, event_data)
-        # else:
-        #     logging.warning(f"Skipping track_event for {event_name} as Application Insights is not configured")
+        else:
+            logging.warning(
+                f"Skipping track_event for {event_name} as Application Insights is not configured"
+            )
     except AttributeError as e:
         # Handle the 'ProxyLogger' object has no attribute 'resource' error
         logging.warning(f"ProxyLogger error in track_event: {e}")

src/backend/kernel_agents/agent_factory.py

@@ -158,7 +158,6 @@ async def create_agent(
 
         # Build the agent definition (functions schema)
         definition = None
-        client = None
 
         try:
             if client is None:
@@ -239,6 +238,7 @@ async def create_all_agents(
         user_id: str,
         temperature: float = 0.0,
         memory_store: Optional[CosmosMemoryContext] = None,
+        client: Optional[Any] = None,
     ) -> Dict[AgentType, BaseAgent]:
         """Create all agent types for a session in a specific order.
 
@@ -265,6 +265,13 @@ async def create_all_agents(
         planner_agent_type = AgentType.PLANNER
         group_chat_manager_type = AgentType.GROUP_CHAT_MANAGER
 
+        try:
+            if client is None:
+                # Create the AIProjectClient instance using the config
+                # This is a placeholder; replace with actual client creation logic
+                client = config.get_ai_project_client()
+        except Exception as client_exc:
+            logger.error(f"Error creating AIProjectClient: {client_exc}")
         # Initialize cache for this session if it doesn't exist
         if session_id not in cls._agent_cache:
             cls._agent_cache[session_id] = {}
@@ -280,6 +287,7 @@ async def create_all_agents(
                 session_id=session_id,
                 user_id=user_id,
                 temperature=temperature,
+                client=client,
                 memory_store=memory_store,
             )
 
@@ -305,6 +313,7 @@ async def create_all_agents(
             user_id=user_id,
             temperature=temperature,
             agent_instances=agent_instances,  # Pass agent instances to the planner
+            client=client,
             response_format=ResponseFormatJsonSchemaType(
                 json_schema=ResponseFormatJsonSchema(
                     name=PlannerResponsePlan.__name__,
@@ -324,6 +333,7 @@ async def create_all_agents(
             session_id=session_id,
             user_id=user_id,
             temperature=temperature,
+            client=client,
             agent_instances=agent_instances,  # Pass agent instances to the planner
         )
         agents[group_chat_manager_type] = group_chat_manager