Feature/memory work (#61)

* refactor(conversation): separate service and repository layers for conversation module

- Split ConversationService and repository/UnitOfWork layers
- Service layer now only handles business logic and orchestration
- Repository layer handles all direct database operations
- UnitOfWork encapsulates transactional operations for messages
- Ensured all public methods have clear English docstrings with arguments, return values, and exceptions

* feat(memory): implement work memory endpoints and services

- Added API routes for conversation count, conversation list, messages, and detail.
- Integrated ConversationService for database queries and LLM-based summary generation.

* feat(memory): implement work memory endpoints and services

- Added API routes for conversation count, conversation list, messages, and detail.
- Integrated ConversationService for database queries and LLM-based summary generation.

* feat(workflow): fix issues causing workflow failures

if-else None value error
knowledge empty list rerank
end node output none node value
assigner input none value

* feat(memory): convert memory file creation time to timestamp and include title and first-line fields in file type

* fix(memory): fix serialization output and default value issues

* fix(workflow): fix issue with hybrid search logic in knowledge retrieval node

api/app/controllers/memory_working_controller.py

@@ -0,0 +1,123 @@
+import uuid
+
+from fastapi import APIRouter, Depends
+from sqlalchemy.orm import Session
+from app.core.response_utils import success, fail
+
+from app.core.logging_config import get_api_logger
+from app.db import get_db
+from app.dependencies import get_current_user
+from app.models import User
+from app.schemas.response_schema import ApiResponse
+from app.services import conversation_service
+from app.services.conversation_service import ConversationService
+
+api_logger = get_api_logger()
+
+router = APIRouter(
+    prefix="/memory/work",
+    tags=["Perceptual Memory System"],
+    dependencies=[Depends(get_current_user)]
+)
+
+
+@router.get("/{group_id}/count", response_model=ApiResponse)
+def get_memory_count(
+        group_id: uuid.UUID,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    pass
+
+
+@router.get("/{group_id}/conversations", response_model=ApiResponse)
+def get_conversations(
+        group_id: uuid.UUID,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """
+    Retrieve all conversations for the current user in a specific group.
+
+    Args:
+        group_id (UUID): The group identifier.
+        current_user (User, optional): The authenticated user.
+        db (Session, optional): SQLAlchemy session.
+
+    Returns:
+        ApiResponse: Contains a list of conversation IDs.
+
+    Notes:
+        - Initializes the ConversationService with the current DB session.
+        - Returns only conversation IDs for lightweight response.
+        - Logs can be added to trace requests in production.
+    """
+    conversation_service = ConversationService(db)
+    conversations = conversation_service.get_user_conversations(
+        group_id,
+        current_user.current_workspace_id
+    )
+    return success(data=[conversation.id for conversation in conversations], msg="get conversations success")
+
+
+@router.get("/{group_id}/messages", response_model=ApiResponse)
+def get_messages(
+        conversation_id: uuid.UUID,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """
+    Retrieve the message history for a specific conversation.
+
+    Args:
+        conversation_id (UUID): The ID of the conversation to fetch messages from.
+        current_user (User, optional): The authenticated user.
+        db (Session, optional): SQLAlchemy session.
+
+    Returns:
+        ApiResponse: Contains the list of messages in the conversation.
+
+    Notes:
+        - Uses ConversationService to fetch messages.
+        - Consider paginating results if message history is large.
+        - Logging can be added for audit and debugging.
+    """
+    conversation_service = ConversationService(db)
+    messages = conversation_service.get_conversation_history(
+        conversation_id,
+    )
+    return success(data=messages, msg="get conversation history success")
+
+
+@router.get("/{group_id}/detail", response_model=ApiResponse)
+async def get_conversation_detail(
+        conversation_id: uuid.UUID,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """
+    Retrieve detailed information about a specific conversation.
+
+    This endpoint will fetch the conversation detail for the user. If the detail
+    does not exist or is outdated, it will trigger the LLM to generate a new summary.
+
+    Args:
+        conversation_id (UUID): The ID of the conversation.
+        current_user (User, optional): The authenticated user making the request.
+        db (Session, optional): SQLAlchemy session.
+
+    Returns:
+        ApiResponse: Contains the conversation detail serialized as a dictionary.
+
+    Notes:
+        - Uses async ConversationService to fetch or generate the conversation detail.
+        - Handles workspace and user-specific context automatically.
+        - Logging and exception handling should be implemented for production monitoring.
+    """
+    conversation_service = ConversationService(db)
+    detail = await conversation_service.get_conversation_detail(
+        user=current_user,
+        conversation_id=conversation_id,
+        workspace_id=current_user.current_workspace_id
+    )
+    return success(data=detail.model_dump(), msg="get conversation detail success")