Testcases

Author: UtkarshMishra-Microsoft

src/backend/agents/agentutils.py

@@ -4,7 +4,7 @@
                                             AzureOpenAIChatCompletionClient)
 from pydantic import BaseModel
 
-from context.cosmos_memory import CosmosBufferedChatCompletionContext
+from src.backend.context.cosmos_memory import CosmosBufferedChatCompletionContext
 from models.messages import InputTask, PlanStatus, Step, StepStatus
 
 common_agent_system_message = "If you do not have the information for the arguments of the function you need to call, do not call the function. Instead, respond back to the user requesting further information. You must not hallucinate or invent any of the information used as arguments in the function. For example, if you need to call a function that requires a delivery address, you must not generate 123 Example St. You must skip calling functions and return a clarification message along the lines of: Sorry, I'm missing some information I need to help you with that. Could you please provide the delivery address so I can do that for you?"

src/backend/agents/tech_support.py

@@ -6,8 +6,8 @@
 from autogen_core.components.tools import FunctionTool, Tool
 from typing_extensions import Annotated
 
-from agents.base_agent import BaseAgent
-from context.cosmos_memory import CosmosBufferedChatCompletionContext
+from src.backend.agents.base_agent import BaseAgent
+from src.backend.context.cosmos_memory import CosmosBufferedChatCompletionContext
 
 formatting_instructions = "Instructions: returning the output of this function call verbatim to the user in markdown. Then write AGENT SUMMARY: and then include a summary of what you did."
 

src/backend/context/cosmos_memory.py

@@ -244,13 +244,13 @@ async def get_messages(self) -> List[LLMMessage]:
                 content = item.get("content", {})
                 message_type = content.get("type")
                 if message_type == "SystemMessage":
-                    message = SystemMessage(**content)
+                    SystemMessage.model_validate(content)
                 elif message_type == "UserMessage":
-                    message = UserMessage(**content)
+                    message = UserMessage.model_validate(content)
                 elif message_type == "AssistantMessage":
-                    message = AssistantMessage(**content)
+                    message = AssistantMessage.model_validate(content)
                 elif message_type == "FunctionExecutionResultMessage":
-                    message = FunctionExecutionResultMessage(**content)
+                    message = FunctionExecutionResultMessage.model_validate(content)
                 else:
                     continue
                 messages.append(message)

src/backend/models/messages.py

@@ -265,13 +265,13 @@ def from_dict(data: dict) -> "GroupChatMessage":
         body_type = body_data.pop("type")
 
         if body_type == "SystemMessage":
-            body = SystemMessage(**body_data)
+            body = SystemMessage.from_dict(body_data)
         elif body_type == "UserMessage":
-            body = UserMessage(**body_data)
+            body = UserMessage.from_dict(body_data)
         elif body_type == "AssistantMessage":
-            body = AssistantMessage(**body_data)
+            body = AssistantMessage.from_dict(body_data)
         elif body_type == "FunctionExecutionResultMessage":
-            body = FunctionExecutionResultMessage(**body_data)
+            body = FunctionExecutionResultMessage.from_dict(body_data)
         else:
             raise ValueError(f"Unknown message type: {body_type}")