fix(workflow): use loose rendering for end-node variables

api/app/core/workflow/nodes/base_node.py

@@ -35,7 +35,7 @@ class WorkflowState(TypedDict):
     # Uses a deep merge function, supporting nested dict updates (e.g., conv.xxx)
     variables: Annotated[dict[str, Any], lambda x, y: {
         **x,
-        **{k: {**x.get(k, {}), **v} if isinstance(v, dict) and isinstance(x.get(k), dict) else v 
+        **{k: {**x.get(k, {}), **v} if isinstance(v, dict) and isinstance(x.get(k), dict) else v
            for k, v in y.items()}
     }]
 
@@ -46,12 +46,12 @@ class WorkflowState(TypedDict):
     # Runtime node variables (simplified version, stores business data for fast access between nodes)
     # Format: {node_id: business_result}
     runtime_vars: Annotated[dict[str, Any], lambda x, y: {**x, **y}]
-    
+
     # Execution context
     execution_id: str
     workspace_id: str
     user_id: str
-    
+
     # Error information (for error edges)
     error: str | None
     error_node: str | None
@@ -66,7 +66,7 @@ class BaseNode(ABC):
     
     所有节点类型都应该继承此基类，实现 execute 方法。
     """
-    
+
     def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
         """初始化节点
         
@@ -83,7 +83,7 @@ class BaseNode(ABC):
         # 使用 or 运算符处理 None 值
         self.config = node_config.get("config") or {}
         self.error_handling = node_config.get("error_handling") or {}
-    
+
     @abstractmethod
     async def execute(self, state: WorkflowState) -> Any:
         """执行节点业务逻辑（非流式）
@@ -108,7 +108,7 @@ class BaseNode(ABC):
             >>> return {"message": "开始", "conversation_id": "xxx"}
         """
         pass
-    
+
     async def execute_stream(self, state: WorkflowState):
         """执行节点业务逻辑（流式）
         
@@ -138,7 +138,7 @@ class BaseNode(ABC):
         result = await self.execute(state)
         # 默认实现：直接 yield 完成标记
         yield {"__final__": True, "result": result}
-    
+
     def supports_streaming(self) -> bool:
         """节点是否支持流式输出
         
@@ -147,7 +147,7 @@ class BaseNode(ABC):
         """
         # 检查子类是否重写了 execute_stream 方法
         return self.execute_stream.__func__ != BaseNode.execute_stream.__func__
-    
+
     def get_timeout(self) -> int:
         """获取超时时间（秒）
         
@@ -156,7 +156,7 @@ class BaseNode(ABC):
         """
         return 60
         # return self.error_handling.get("timeout", 60)
-    
+
     async def run(self, state: WorkflowState) -> dict[str, Any]:
         """执行节点（带错误处理和输出包装，非流式）
         
@@ -173,33 +173,33 @@ class BaseNode(ABC):
             标准化的状态更新字典
         """
         import time
-        
+
         start_time = time.time()
 
         timeout = self.get_timeout()
-        
+
         try:
             # 调用节点的业务逻辑
             business_result = await asyncio.wait_for(
                 self.execute(state),
                 timeout=timeout
             )
-            
+
             elapsed_time = time.time() - start_time
-            
+
             # 提取处理后的输出（调用子类的 _extract_output）
             extracted_output = self._extract_output(business_result)
-            
+
             # 包装成标准输出格式
             wrapped_output = self._wrap_output(business_result, elapsed_time, state)
-            
+
             # 将提取后的输出存储到运行时变量中（供后续节点快速访问）
             # 如果提取后的输出是字典，拆包存储；否则存储为 output 字段
             if isinstance(extracted_output, dict):
                 runtime_var = extracted_output
             else:
                 runtime_var = {"output": extracted_output}
-            
+
             # 返回包装后的输出和运行时变量
             return {
                 **wrapped_output,
@@ -208,7 +208,7 @@ class BaseNode(ABC):
                 },
                 "looping": state["looping"]
             }
-            
+
         except TimeoutError:
             elapsed_time = time.time() - start_time
             logger.error(f"节点 {self.node_id} 执行超时（{timeout}秒）")
@@ -217,7 +217,7 @@ class BaseNode(ABC):
             elapsed_time = time.time() - start_time
             logger.error(f"节点 {self.node_id} 执行失败: {e}", exc_info=True)
             return self._wrap_error(str(e), elapsed_time, state)
-    
+
     async def run_stream(self, state: WorkflowState):
         """Execute node with error handling and output wrapping (streaming)
         
@@ -240,40 +240,41 @@ class BaseNode(ABC):
             State updates with streaming buffer and final result
         """
         import time
-        
+
         start_time = time.time()
 
         timeout = self.get_timeout()
-        
+
         try:
             # Get LangGraph's stream writer for sending custom data
             writer = get_stream_writer()
-            
+
             # Check if this is an End node
             # End nodes CAN send chunks (for suffix), but only after LLM content
             is_end_node = self.node_type == "end"
-            
+
             # Check if this node is adjacent to End node (for message type)
             is_adjacent_to_end = getattr(self, '_is_adjacent_to_end', False)
-            
+
             # Determine chunk type: "message" for End and adjacent nodes, "node_chunk" for others
             chunk_type = "message" if (is_end_node or is_adjacent_to_end) else "node_chunk"
-            
+
-            logger.debug(f"节点 {self.node_id} chunk 类型: {chunk_type} (is_end={is_end_node}, adjacent={is_adjacent_to_end})")
+            logger.debug(
-            
+                f"节点 {self.node_id} chunk 类型: {chunk_type} (is_end={is_end_node}, adjacent={is_adjacent_to_end})")
+
             # Accumulate complete result (for final wrapping)
             chunks = []
             final_result = None
             chunk_count = 0
-            
+
             # Stream chunks in real-time
             loop_start = asyncio.get_event_loop().time()
-            
+
             async for item in self.execute_stream(state):
                 # Check timeout
                 if asyncio.get_event_loop().time() - loop_start > timeout:
                     raise TimeoutError()
-                
+
                 # Check if it's a completion marker
                 if isinstance(item, dict) and item.get("__final__"):
                     final_result = item["result"]
@@ -282,10 +283,10 @@ class BaseNode(ABC):
                     chunk_count += 1
                     chunks.append(item)
                     full_content = "".join(chunks)
-                    
+
                     # Send chunks for all nodes (including End nodes for suffix)
                     logger.debug(f"节点 {self.node_id} 发送 chunk #{chunk_count}: {item[:50]}...")
-                    
+
                     # 1. Send via stream writer (for real-time client updates)
                     writer({
                         "type": chunk_type,  # "message" or "node_chunk"
@@ -294,7 +295,7 @@ class BaseNode(ABC):
                         "full_content": full_content,
                         "chunk_index": chunk_count
                     })
-                    
+
                     # 2. Update streaming buffer in state (for downstream nodes)
                     # Only non-End nodes need streaming buffer
                     if not is_end_node:
@@ -313,7 +314,7 @@ class BaseNode(ABC):
                     chunk_str = str(item)
                     chunks.append(chunk_str)
                     full_content = "".join(chunks)
-                    
+
                     # Send chunks for all nodes
                     writer({
                         "type": chunk_type,  # "message" or "node_chunk"
@@ -322,7 +323,7 @@ class BaseNode(ABC):
                         "full_content": full_content,
                         "chunk_index": chunk_count
                     })
-                    
+
                     # Only non-End nodes need streaming buffer
                     if not is_end_node:
                         yield {
@@ -334,23 +335,23 @@ class BaseNode(ABC):
                                 }
                             }
                         }
-            
+
             elapsed_time = time.time() - start_time
-            
+
             logger.info(f"节点 {self.node_id} 流式执行完成，耗时: {elapsed_time:.2f}s, chunks: {chunk_count}")
-            
+
             # Extract processed output (call subclass's _extract_output)
             extracted_output = self._extract_output(final_result)
-            
+
             # Wrap final result
             final_output = self._wrap_output(final_result, elapsed_time, state)
-            
+
             # Store extracted output in runtime variables (for quick access by subsequent nodes)
             if isinstance(extracted_output, dict):
                 runtime_var = extracted_output
             else:
                 runtime_var = {"output": extracted_output}
-            
+
             # Build complete state update (including node_outputs, runtime_vars, and final streaming buffer)
             state_update = {
                 **final_output,
@@ -359,7 +360,7 @@ class BaseNode(ABC):
                 },
                 "looping": state["looping"]
             }
-            
+
             # Add streaming buffer for non-End nodes
             if not is_end_node:
                 state_update["streaming_buffer"] = {
@@ -369,11 +370,11 @@ class BaseNode(ABC):
                         "is_complete": True  # Mark as complete
                     }
                 }
-            
+
             # Finally yield state update
             # LangGraph will merge this into state
             yield state_update
-                
+
         except TimeoutError:
             elapsed_time = time.time() - start_time
             logger.error(f"节点 {self.node_id} 执行超时 ({timeout}s)")
@@ -384,12 +385,12 @@ class BaseNode(ABC):
             logger.error(f"节点 {self.node_id} 执行失败: {e}", exc_info=True)
             error_output = self._wrap_error(str(e), elapsed_time, state)
             yield error_output
-    
+
     def _wrap_output(
-        self, 
+            self,
-        business_result: Any, 
+            business_result: Any,
-        elapsed_time: float,
+            elapsed_time: float,
-        state: WorkflowState
+            state: WorkflowState
     ) -> dict[str, Any]:
         """将业务结果包装成标准输出格式
         
@@ -403,13 +404,13 @@ class BaseNode(ABC):
         """
         # 提取输入数据（用于记录）
         input_data = self._extract_input(state)
-        
+
         # 提取 token 使用情况（如果有）
         token_usage = self._extract_token_usage(business_result)
-        
+
         # 提取实际输出（去除元数据）
         output = self._extract_output(business_result)
-        
+
         # 构建标准节点输出
         node_output = {
             "node_id": self.node_id,
@@ -422,18 +423,18 @@ class BaseNode(ABC):
             "token_usage": token_usage,
             "error": None
         }
-        
+
         return {
             "node_outputs": {
                 self.node_id: node_output
             }
         }
-    
+
     def _wrap_error(
-        self, 
+            self,
-        error_message: str, 
+            error_message: str,
-        elapsed_time: float,
+            elapsed_time: float,
-        state: WorkflowState
+            state: WorkflowState
     ) -> dict[str, Any]:
         """将错误包装成标准输出格式
         
@@ -447,10 +448,10 @@ class BaseNode(ABC):
         """
         # 查找错误边
         error_edge = self._find_error_edge()
-        
+
         # 提取输入数据
         input_data = self._extract_input(state)
-        
+
         # 构建错误输出
         node_output = {
             "node_id": self.node_id,
@@ -463,7 +464,7 @@ class BaseNode(ABC):
             "token_usage": None,
             "error": error_message
         }
-        
+
         if error_edge:
             # 有错误边：记录错误并继续
             logger.warning(
@@ -480,7 +481,7 @@ class BaseNode(ABC):
             # 无错误边：抛出异常停止工作流
             logger.error(f"节点 {self.node_id} 执行失败，停止工作流: {error_message}")
             raise Exception(f"节点 {self.node_id} 执行失败: {error_message}")
-    
+
     def _extract_input(self, state: WorkflowState) -> dict[str, Any]:
         """提取节点输入数据（用于记录）
         
@@ -494,7 +495,7 @@ class BaseNode(ABC):
         """
         # 默认返回配置
         return {"config": self.config}
-    
+
     def _extract_output(self, business_result: Any) -> Any:
         """从业务结果中提取实际输出
         
@@ -508,7 +509,7 @@ class BaseNode(ABC):
         """
         # 默认直接返回业务结果
         return business_result
-    
+
     def _extract_token_usage(self, business_result: Any) -> dict[str, int] | None:
         """从业务结果中提取 token 使用情况
         
@@ -522,7 +523,7 @@ class BaseNode(ABC):
         """
         # 默认返回 None
         return None
-    
+
     def _find_error_edge(self) -> dict[str, Any] | None:
         """查找错误边
         
@@ -533,8 +534,8 @@ class BaseNode(ABC):
             if edge.get("source") == self.node_id and edge.get("type") == "error":
                 return edge
         return None
-    
+
-    def _render_template(self, template: str, state: WorkflowState | None, struct: bool = True) -> str:
+    def _render_template(self, template: str, state: WorkflowState | None, strict: bool = True) -> str:
         """渲染模板
         
         支持的变量命名空间：
@@ -550,28 +551,28 @@ class BaseNode(ABC):
             渲染后的字符串
         """
         from app.core.workflow.template_renderer import render_template
-        
+
         # 处理 state 为 None 的情况
         if state is None:
             state = {}
-        
+
         # 使用变量池获取变量
         pool = VariablePool(state)
-        
+
         # 构建完整的 variables 结构
         variables = {
             "sys": pool.get_all_system_vars(),
             "conv": pool.get_all_conversation_vars()
         }
-        
+
         return render_template(
             template=template,
             variables=variables,
             node_outputs=pool.get_all_node_outputs(),
             system_vars=pool.get_all_system_vars(),
-            struct=struct
+            strict=strict
         )
-    
+
     def _evaluate_condition(self, expression: str, state: WorkflowState | None) -> bool:
         """评估条件表达式
         
@@ -588,20 +589,20 @@ class BaseNode(ABC):
             布尔值结果
         """
         from app.core.workflow.expression_evaluator import evaluate_condition
-        
+
         # 处理 state 为 None 的情况
         if state is None:
             state = {}
-        
+
         # 使用变量池获取变量
         pool = VariablePool(state)
-        
+
         # 构建完整的 variables 结构（包含 sys 和 conv）
         variables = {
             "sys": pool.get_all_system_vars(),
             "conv": pool.get_all_conversation_vars()
         }
-        
+
         return evaluate_condition(
             expression=expression,
             variables=variables,
@@ -626,12 +627,12 @@ class BaseNode(ABC):
             >>> llm_output = pool.get("llm_qa.output")
         """
         return VariablePool(state)
-    
+
     def get_variable(
-        self, 
+            self,
-        selector: list[str] | str, 
+            selector: list[str] | str,
-        state: WorkflowState,
+            state: WorkflowState,
-        default: Any = None
+            default: Any = None
     ) -> Any:
         """获取变量值（便捷方法）
         
@@ -650,7 +651,7 @@ class BaseNode(ABC):
         """
         pool = VariablePool(state)
         return pool.get(selector, default=default)
-    
+
     def has_variable(self, selector: list[str] | str, state: WorkflowState) -> bool:
         """检查变量是否存在（便捷方法）
         

api/app/core/workflow/nodes/end/node.py

@@ -37,7 +37,7 @@ class EndNode(BaseNode):
 
         # 如果配置了输出模板，使用模板渲染；否则使用默认输出
         if output_template:
-            output = self._render_template(output_template, state, struct=False)
+            output = self._render_template(output_template, state, strict=False)
         else:
             output = "工作流已完成"
 
@@ -156,6 +156,16 @@ class EndNode(BaseNode):
 
         if not output_template:
             output = "工作流已完成"
+            from langgraph.config import get_stream_writer
+            writer = get_stream_writer()
+            writer({
+                "type": "message",  # End node output uses message type
+                "node_id": self.node_id,
+                "chunk": "",
+                "full_content": output,
+                "chunk_index": 1,
+                "is_suffix": False
+            })
             yield {"__final__": True, "result": output}
             return
 
@@ -190,7 +200,7 @@ class EndNode(BaseNode):
 
         if upstream_llm_ref_index is None:
             # No reference to direct upstream LLM node, output complete template content
-            output = self._render_template(output_template, state)
+            output = self._render_template(output_template, state, strict=False)
             logger.info(f"节点 {self.node_id} 没有引用直接上游 LLM 节点，输出完整内容: '{output[:50]}...'")
 
             # Send complete content via writer (as a single message chunk)
@@ -246,7 +256,7 @@ class EndNode(BaseNode):
         suffix = "".join(suffix_parts)
 
         # 构建完整输出（用于返回，包含前缀 + 动态内容 + 后缀）
-        full_output = self._render_template(output_template, state)
+        full_output = self._render_template(output_template, state, strict=False)
 
         logger.info(f"[后缀调试] 节点 {self.node_id} 后缀部分数量: {len(suffix_parts)}")
         logger.info(f"[后缀调试] 后缀内容: '{suffix}'")

api/app/core/workflow/template_renderer.py

@@ -5,6 +5,7 @@
 """
 
 import logging
+from collections import defaultdict
 from typing import Any
 
 from jinja2 import TemplateSyntaxError, UndefinedError, Environment, StrictUndefined, Undefined
@@ -12,6 +13,18 @@ from jinja2 import TemplateSyntaxError, UndefinedError, Environment, StrictUndef
 logger = logging.getLogger(__name__)
 
 
+class SafeUndefined(Undefined):
+    """访问未定义属性不会报错，返回空字符串"""
+    __slots__ = ()
+
+    def _fail_with_undefined_error(self, *args, **kwargs):
+        return ""
+
+    __add__ = __radd__ = __mul__ = __rmul__ = __div__ = __rdiv__ = __truediv__ = __rtruediv__ = _fail_with_undefined_error
+    __getitem__ = __getattr__ = _fail_with_undefined_error
+    __str__ = __repr__ = lambda self: ""
+
+
 class TemplateRenderer:
     """模板渲染器"""
 
@@ -21,8 +34,9 @@ class TemplateRenderer:
         Args:
             strict: 是否使用严格模式（未定义变量会抛出异常）
         """
+        self.strict = strict
         self.env = Environment(
-            undefined=StrictUndefined if strict else Undefined,
+            undefined=StrictUndefined if strict else SafeUndefined,
             autoescape=False  # 不自动转义，因为我们处理的是文本而非 HTML
         )
 
@@ -69,12 +83,17 @@ class TemplateRenderer:
         # variables 的结构：{"sys": {...}, "conv": {...}}
         sys_vars = variables.get("sys", {}) if isinstance(variables, dict) else {}
         conv_vars = variables.get("conv", {}) if isinstance(variables, dict) else {}
-
+        if self.strict:
-        context = {
+            context = defaultdict(dict)
-            "conv": conv_vars,  # 会话变量：{{conv.user_name}}
+            context["conv"] = conv_vars
-            "node": node_outputs,  # 节点输出：{{node.node_1.output}}
+            context["nodes"] = node_outputs
-            "sys": {**(system_vars or {}), **sys_vars},  # 系统变量：{{sys.execution_id}}（合并两个来源）
+            context["sys"] = {**(system_vars or {}), **sys_vars}
-        }
+        else:
+            context = {
+                "conv": conv_vars,  # 会话变量：{{conv.user_name}}
+                "node": node_outputs,  # 节点输出：{{node.node_1.output}}
+                "sys": {**(system_vars or {}), **sys_vars},  # 系统变量：{{sys.execution_id}}（合并两个来源）
+            }
 
         # 支持直接通过节点ID访问节点输出：{{llm_qa.output}}
         # 将所有节点输出添加到顶层上下文
@@ -141,12 +160,12 @@ def render_template(
         variables: dict[str, Any],
         node_outputs: dict[str, Any],
         system_vars: dict[str, Any] | None = None,
-        struct: bool = True
+        strict: bool = True
 ) -> str:
     """渲染模板（便捷函数）
     
     Args:
-        struct: 渲染模式
+        strict: 严格模式
         template: 模板字符串
         variables: 用户变量
         node_outputs: 节点输出
@@ -164,7 +183,7 @@ def render_template(
         ... )
         '请分析: 这是一段文本'
     """
-    renderer = TemplateRenderer(strict=struct)
+    renderer = TemplateRenderer(strict=strict)
     return renderer.render(template, variables, node_outputs, system_vars)
 
 

api/app/templates/workflows/simple_qa/template.yml

@@ -53,7 +53,7 @@ nodes:
     type: end
     name: 结束
     config:
-      output: "{{llm_qa.output}}"
+      output: "{{ llm_qa.output }}"
     position:
       x: 900
       y: 100