test: 补充权限、消息与会话服务回归用例

会话读取、接管、重分配与 Socket 订阅统一校验角色、组织与可见性。

backend/tests/services/test_workflow_execution_end_chat.py

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+"""
+Copyright 2024-2026 ChatterMate
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import pytest
+from unittest.mock import Mock, patch, AsyncMock, MagicMock
+from uuid import uuid4
+from app.services.workflow_execution import WorkflowExecutionService
+from app.models.workflow_node import WorkflowNode, NodeType, ExitCondition
+from app.agents.chat_agent import ChatResponse
+from app.models.workflow import Workflow, WorkflowStatus
+
+
+@pytest.fixture
+def mock_db():
+    return Mock()
+
+
+@pytest.fixture
+def workflow_service(mock_db):
+    return WorkflowExecutionService(mock_db)
+
+
+@pytest.fixture
+def sample_workflow():
+    workflow = MagicMock(spec=Workflow)
+    workflow.id = uuid4()
+    workflow.name = "Test Workflow"
+    workflow.nodes = []  # List to store nodes
+    workflow.connections = []  # List to store connections
+    workflow.status = WorkflowStatus.PUBLISHED
+    return workflow
+
+
+class TestWorkflowExecutionEndChat:
+    
+    @pytest.mark.asyncio
+    async def test_llm_node_end_chat_with_rating_config_true(self, workflow_service, sample_workflow):
+        """Test LLM node end chat with ask_for_rating config set to True"""
+        
+        # Create LLM node with continuous execution and ask_for_rating: True
+        node = Mock(spec=WorkflowNode)
+        node.id = uuid4()
+        node.node_type = NodeType.LLM
+        config_dict = {
+            "system_prompt": "You are a helpful assistant.",
+            "exit_condition": ExitCondition.CONTINUOUS_EXECUTION,
+            "ask_for_rating": True,
+            "model_config": {
+                "temperature": 0.7,
+                "max_tokens": 150
+            }
+        }
+        node.config = MagicMock()
+        node.config.__getitem__.side_effect = config_dict.__getitem__
+        node.config.get.side_effect = config_dict.get
+        
+        # Mock chat agent and its response
+        mock_chat_agent = AsyncMock()
+        mock_response = Mock(spec=ChatResponse)
+        mock_response.message = "Thank you for chatting with us. Goodbye!"
+        mock_response.transfer_to_human = False
+        mock_response.end_chat = True
+        mock_response.request_rating = False  # This should be updated by _handle_end_chat
+        mock_response.transfer_reason = None
+        mock_response.transfer_description = None
+        mock_response.success = True  # Add success attribute
+        
+        # Mock the updated response after _handle_end_chat
+        mock_updated_response = Mock(spec=ChatResponse)
+        mock_updated_response.message = "Thank you for chatting with us. Goodbye!\n\nThank you for chatting with us! Would you please take a moment to rate your experience? Your feedback helps us improve our service."
+        mock_updated_response.transfer_to_human = False
+        mock_updated_response.end_chat = True
+        mock_updated_response.request_rating = True  # Updated by _handle_end_chat
+        mock_updated_response.transfer_reason = None
+        mock_updated_response.transfer_description = None
+        mock_updated_response.success = True  # Add success attribute
+        
+        # Set up async method return values
+        mock_chat_agent._get_llm_response_only = AsyncMock(return_value=mock_response)
+        mock_chat_agent._handle_end_chat = AsyncMock(return_value=mock_updated_response)
+        
+        # Mock _find_next_node
+        next_node_id = uuid4()
+        with patch('app.services.workflow_execution.ChatAgent') as mock_chat_agent_class:
+            mock_chat_agent_class.create_async = AsyncMock(return_value=mock_chat_agent)
+            with patch.object(workflow_service.session_repo, 'get_workflow_history', return_value=[]), \
+                 patch.object(workflow_service, '_find_next_node', return_value=next_node_id):
+                result = await workflow_service._execute_llm_node(
+                    node=node,
+                    workflow=sample_workflow,
+                    workflow_state={},
+                    user_message="Goodbye",
+                    api_key="test-key",
+                    model_name="gpt-4",
+                    model_type="openai",
+                    base_url=None,
+                    org_id="test-org",
+                    agent_id="test-agent",
+                    customer_id="test-customer",
+                    session_id="test-session"
+                )
+                
+                # Verify _handle_end_chat was NOT called (it's handled in workflow_chat service)
+                mock_chat_agent._handle_end_chat.assert_not_called()
+                
+                # Verify the result has the correct rating setting
+                assert result.success is True
+                assert result.end_chat is True
+                assert result.request_rating is True
+                assert result.next_node_id == next_node_id
+    
+    @pytest.mark.asyncio
+    async def test_llm_node_end_chat_with_rating_config_false(self, workflow_service, sample_workflow):
+        """Test LLM node end chat with ask_for_rating config set to False"""
+        
+        # Create LLM node with continuous execution and ask_for_rating: False
+        node = Mock(spec=WorkflowNode)
+        node.id = uuid4()
+        node.node_type = NodeType.LLM
+        config_dict = {
+            "system_prompt": "You are a helpful assistant.",
+            "exit_condition": ExitCondition.CONTINUOUS_EXECUTION,
+            "ask_for_rating": False,
+            "model_config": {
+                "temperature": 0.7,
+                "max_tokens": 150
+            }
+        }
+        node.config = MagicMock()
+        node.config.__getitem__.side_effect = config_dict.__getitem__
+        node.config.get.side_effect = config_dict.get
+        node.config.__contains__ = lambda *args: args[1] in config_dict  # Fix lambda to handle multiple args
+        
+        # Mock chat agent and its response
+        mock_chat_agent = AsyncMock()
+        mock_response = Mock(spec=ChatResponse)
+        mock_response.message = "Thank you for chatting with us. Goodbye!"
+        mock_response.transfer_to_human = False
+        mock_response.end_chat = True
+        mock_response.request_rating = True  # This should be updated by _handle_end_chat
+        mock_response.transfer_reason = None
+        mock_response.transfer_description = None
+        mock_response.success = True  # Add success attribute
+        
+        # Mock the updated response after _handle_end_chat
+        mock_updated_response = Mock(spec=ChatResponse)
+        mock_updated_response.message = "Thank you for chatting with us. Goodbye!"
+        mock_updated_response.transfer_to_human = False
+        mock_updated_response.end_chat = True
+        mock_updated_response.request_rating = False  # Updated by _handle_end_chat to False
+        mock_updated_response.transfer_reason = None
+        mock_updated_response.transfer_description = None
+        mock_updated_response.success = True  # Add success attribute
+        
+        # Set up async method return values
+        mock_chat_agent._get_llm_response_only = AsyncMock(return_value=mock_response)
+        mock_chat_agent._handle_end_chat = AsyncMock(return_value=mock_updated_response)
+        
+        # Mock _find_next_node
+        next_node_id = uuid4()
+        with patch('app.services.workflow_execution.ChatAgent') as mock_chat_agent_class:
+            mock_chat_agent_class.create_async = AsyncMock(return_value=mock_chat_agent)
+            with patch.object(workflow_service.session_repo, 'get_workflow_history', return_value=[]), \
+                 patch.object(workflow_service, '_find_next_node', return_value=next_node_id):
+                result = await workflow_service._execute_llm_node(
+                    node=node,
+                    workflow=sample_workflow,
+                    workflow_state={},
+                    user_message="Goodbye",
+                    api_key="test-key",
+                    model_name="gpt-4",
+                    model_type="openai",
+                    base_url=None,
+                    org_id="test-org",
+                    agent_id="test-agent",
+                    customer_id="test-customer",
+                    session_id="test-session"
+                )
+                
+                # Verify _handle_end_chat was NOT called (it's handled in workflow_chat service)
+                mock_chat_agent._handle_end_chat.assert_not_called()
+                
+                # Verify the result has the correct rating setting
+                assert result.success is True
+                assert result.end_chat is True
+                assert result.request_rating is False
+                assert result.next_node_id == next_node_id
+    
+    @pytest.mark.asyncio
+    async def test_llm_node_end_chat_without_rating_config(self, workflow_service, sample_workflow):
+        """Test LLM node end chat without ask_for_rating config (should default to True)"""
+        
+        # Create LLM node with continuous execution but no ask_for_rating config
+        node = Mock(spec=WorkflowNode)
+        node.id = uuid4()
+        node.node_type = NodeType.LLM
+        config_dict = {
+            "system_prompt": "You are a helpful assistant.",
+            "exit_condition": ExitCondition.CONTINUOUS_EXECUTION,
+            "model_config": {
+                "temperature": 0.7,
+                "max_tokens": 150
+            }
+        }
+        node.config = MagicMock()
+        node.config.__getitem__.side_effect = config_dict.__getitem__
+        node.config.get.side_effect = config_dict.get
+        
+        # Mock chat agent and its response
+        mock_chat_agent = AsyncMock()
+        mock_response = Mock(spec=ChatResponse)
+        mock_response.message = "Thank you for chatting with us. Goodbye!"
+        mock_response.transfer_to_human = False
+        mock_response.end_chat = True
+        mock_response.request_rating = False
+        mock_response.transfer_reason = None
+        mock_response.transfer_description = None
+        mock_response.success = True  # Add success attribute
+        
+        # Mock the updated response after _handle_end_chat
+        mock_updated_response = Mock(spec=ChatResponse)
+        mock_updated_response.message = "Thank you for chatting with us. Goodbye!\n\nThank you for chatting with us! Would you please take a moment to rate your experience? Your feedback helps us improve our service."
+        mock_updated_response.transfer_to_human = False
+        mock_updated_response.end_chat = True
+        mock_updated_response.request_rating = True  # Should default to True
+        mock_updated_response.transfer_reason = None
+        mock_updated_response.transfer_description = None
+        mock_updated_response.success = True  # Add success attribute
+        
+        # Set up async method return values
+        mock_chat_agent._get_llm_response_only = AsyncMock(return_value=mock_response)
+        mock_chat_agent._handle_end_chat = AsyncMock(return_value=mock_updated_response)
+        
+        # Mock _find_next_node
+        next_node_id = uuid4()
+        with patch('app.services.workflow_execution.ChatAgent') as mock_chat_agent_class:
+            mock_chat_agent_class.create_async = AsyncMock(return_value=mock_chat_agent)
+            with patch.object(workflow_service.session_repo, 'get_workflow_history', return_value=[]), \
+                 patch.object(workflow_service, '_find_next_node', return_value=next_node_id):
+                result = await workflow_service._execute_llm_node(
+                    node=node,
+                    workflow=sample_workflow,
+                    workflow_state={},
+                    user_message="Goodbye",
+                    api_key="test-key",
+                    model_name="gpt-4",
+                    model_type="openai",
+                    base_url=None,
+                    org_id="test-org",
+                    agent_id="test-agent",
+                    customer_id="test-customer",
+                    session_id="test-session"
+                )
+                
+                # Verify _handle_end_chat was NOT called (it's handled in workflow_chat service)
+                mock_chat_agent._handle_end_chat.assert_not_called()
+                
+                # Verify the result has the correct rating setting (default True)
+                assert result.success is True
+                assert result.end_chat is True
+                assert result.request_rating is True
+                assert result.next_node_id == next_node_id
+    
+    @pytest.mark.asyncio
+    async def test_llm_node_end_chat_single_execution_no_config_override(self, workflow_service, sample_workflow):
+        """Test LLM node end chat with single execution (should not use config override)"""
+        
+        # Create LLM node with single execution
+        node = Mock(spec=WorkflowNode)
+        node.id = uuid4()
+        node.node_type = NodeType.LLM
+        config_dict = {
+            "system_prompt": "You are a helpful assistant.",
+            "exit_condition": ExitCondition.SINGLE_EXECUTION,
+            "ask_for_rating": False,  # This should not be used for single execution
+            "model_config": {
+                "temperature": 0.7,
+                "max_tokens": 150
+            }
+        }
+        node.config = MagicMock()
+        node.config.__getitem__.side_effect = config_dict.__getitem__
+        node.config.get.side_effect = config_dict.get
+        
+        # Mock chat agent and its response
+        mock_chat_agent = AsyncMock()
+        mock_response = Mock(spec=ChatResponse)
+        mock_response.message = "Thank you for chatting with us. Goodbye!"
+        mock_response.transfer_to_human = False
+        mock_response.end_chat = True
+        mock_response.request_rating = False
+        mock_response.transfer_reason = None
+        mock_response.transfer_description = None
+        mock_response.success = True  # Add success attribute
+        
+        # Mock the updated response after _handle_end_chat (using agent's default)
+        mock_updated_response = Mock(spec=ChatResponse)
+        mock_updated_response.message = "Thank you for chatting with us. Goodbye!"
+        mock_updated_response.transfer_to_human = False
+        mock_updated_response.end_chat = True
+        mock_updated_response.request_rating = False  # Uses agent's default, not config
+        mock_updated_response.transfer_reason = None
+        mock_updated_response.transfer_description = None
+        mock_updated_response.success = True  # Add success attribute
+        
+        # Set up async method return values
+        mock_chat_agent._get_llm_response_only = AsyncMock(return_value=mock_response)
+        mock_chat_agent._handle_end_chat = AsyncMock(return_value=mock_updated_response)
+        
+        # Mock _find_next_node
+        next_node_id = uuid4()
+        with patch('app.services.workflow_execution.ChatAgent') as mock_chat_agent_class:
+            mock_chat_agent_class.create_async = AsyncMock(return_value=mock_chat_agent)
+            with patch.object(workflow_service.session_repo, 'get_workflow_history', return_value=[]), \
+                 patch.object(workflow_service, '_find_next_node', return_value=next_node_id):
+                result = await workflow_service._execute_llm_node(
+                    node=node,
+                    workflow=sample_workflow,
+                    workflow_state={},
+                    user_message="Goodbye",
+                    api_key="test-key",
+                    model_name="gpt-4",
+                    model_type="openai",
+                    base_url=None,
+                    org_id="test-org",
+                    agent_id="test-agent",
+                    customer_id="test-customer",
+                    session_id="test-session"
+                )
+                
+                # Verify _handle_end_chat was NOT called (it's handled in workflow_chat service)
+                mock_chat_agent._handle_end_chat.assert_not_called()
+                
+                # Verify the result (single execution doesn't override rating)
+                assert result.success is True
+                assert result.end_chat is True
+                assert result.request_rating is False
+                assert result.next_node_id == next_node_id
+    
+    @pytest.mark.asyncio
+    async def test_llm_node_no_end_chat_no_handle_end_chat_call(self, workflow_service, sample_workflow):
+        """Test LLM node without end chat (should not call _handle_end_chat)"""
+        
+        # Create LLM node
+        node = Mock(spec=WorkflowNode)
+        node.id = uuid4()
+        node.node_type = NodeType.LLM
+        config_dict = {
+            "system_prompt": "You are a helpful assistant.",
+            "exit_condition": ExitCondition.CONTINUOUS_EXECUTION,
+            "model_config": {
+                "temperature": 0.7,
+                "max_tokens": 150
+            }
+        }
+        node.config = MagicMock()
+        node.config.__getitem__.side_effect = config_dict.__getitem__
+        node.config.get.side_effect = config_dict.get
+        
+        # Mock chat agent and its response (no end chat)
+        mock_chat_agent = AsyncMock()
+        mock_response = Mock(spec=ChatResponse)
+        mock_response.message = "How can I help you today?"
+        mock_response.transfer_to_human = False
+        mock_response.end_chat = False  # No end chat
+        mock_response.request_rating = False
+        mock_response.transfer_reason = None
+        mock_response.transfer_description = None
+        mock_response.success = True  # Add success attribute
+        
+        # Set up async method return values
+        mock_chat_agent._get_llm_response_only = AsyncMock(return_value=mock_response)
+        
+        # Mock _find_next_node
+        next_node_id = uuid4()
+        with patch('app.services.workflow_execution.ChatAgent') as mock_chat_agent_class:
+            mock_chat_agent_class.create_async = AsyncMock(return_value=mock_chat_agent)
+            with patch.object(workflow_service.session_repo, 'get_workflow_history', return_value=[]), \
+                 patch.object(workflow_service, '_find_next_node', return_value=next_node_id):
+                result = await workflow_service._execute_llm_node(
+                    node=node,
+                    workflow=sample_workflow,
+                    workflow_state={},
+                    user_message="Hello",
+                    api_key="test-key",
+                    model_name="gpt-4",
+                    model_type="openai",
+                    base_url=None,
+                    org_id="test-org",
+                    agent_id="test-agent",
+                    customer_id="test-customer",
+                    session_id="test-session"
+                )
+                
+                # Verify _handle_end_chat was NOT called
+                mock_chat_agent._handle_end_chat.assert_not_called()
+                
+                # Verify the result
+                assert result.success is True
+                assert result.end_chat is False
+                assert result.request_rating is False
+                assert result.next_node_id is None