fix(workflow): use loose rendering for end-node variables
This commit is contained in:
mengyonghao
@@ -35,7 +35,7 @@ class WorkflowState(TypedDict):
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# Uses a deep merge function, supporting nested dict updates (e.g., conv.xxx)
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variables: Annotated[dict[str, Any], lambda x, y: {
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**x,
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**{k: {**x.get(k, {}), **v} if isinstance(v, dict) and isinstance(x.get(k), dict) else v
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**{k: {**x.get(k, {}), **v} if isinstance(v, dict) and isinstance(x.get(k), dict) else v
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for k, v in y.items()}
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}]
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@@ -46,12 +46,12 @@ class WorkflowState(TypedDict):
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# Runtime node variables (simplified version, stores business data for fast access between nodes)
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# Format: {node_id: business_result}
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runtime_vars: Annotated[dict[str, Any], lambda x, y: {**x, **y}]
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# Execution context
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execution_id: str
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workspace_id: str
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user_id: str
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# Error information (for error edges)
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error: str | None
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error_node: str | None
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@@ -66,7 +66,7 @@ class BaseNode(ABC):
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所有节点类型都应该继承此基类，实现 execute 方法。
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"""
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def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
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"""初始化节点
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@@ -83,7 +83,7 @@ class BaseNode(ABC):
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# 使用 or 运算符处理 None 值
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self.config = node_config.get("config") or {}
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self.error_handling = node_config.get("error_handling") or {}
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@abstractmethod
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async def execute(self, state: WorkflowState) -> Any:
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"""执行节点业务逻辑（非流式）
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@@ -108,7 +108,7 @@ class BaseNode(ABC):
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>>> return {"message": "开始", "conversation_id": "xxx"}
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"""
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pass
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async def execute_stream(self, state: WorkflowState):
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"""执行节点业务逻辑（流式）
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@@ -138,7 +138,7 @@ class BaseNode(ABC):
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result = await self.execute(state)
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# 默认实现：直接 yield 完成标记
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yield {"__final__": True, "result": result}
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def supports_streaming(self) -> bool:
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"""节点是否支持流式输出
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@@ -147,7 +147,7 @@ class BaseNode(ABC):
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"""
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# 检查子类是否重写了 execute_stream 方法
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return self.execute_stream.__func__ != BaseNode.execute_stream.__func__
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def get_timeout(self) -> int:
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"""获取超时时间（秒）
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@@ -156,7 +156,7 @@ class BaseNode(ABC):
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"""
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return 60
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# return self.error_handling.get("timeout", 60)
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async def run(self, state: WorkflowState) -> dict[str, Any]:
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"""执行节点（带错误处理和输出包装，非流式）
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@@ -173,33 +173,33 @@ class BaseNode(ABC):
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标准化的状态更新字典
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"""
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import time
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start_time = time.time()
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timeout = self.get_timeout()
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try:
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# 调用节点的业务逻辑
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business_result = await asyncio.wait_for(
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self.execute(state),
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timeout=timeout
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)
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elapsed_time = time.time() - start_time
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# 提取处理后的输出（调用子类的 _extract_output）
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extracted_output = self._extract_output(business_result)
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# 包装成标准输出格式
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wrapped_output = self._wrap_output(business_result, elapsed_time, state)
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# 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）
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# 如果提取后的输出是字典，拆包存储；否则存储为 output 字段
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if isinstance(extracted_output, dict):
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runtime_var = extracted_output
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else:
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runtime_var = {"output": extracted_output}
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# 返回包装后的输出和运行时变量
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return {
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**wrapped_output,
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@@ -208,7 +208,7 @@ class BaseNode(ABC):
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},
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"looping": state["looping"]
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}
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except TimeoutError:
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elapsed_time = time.time() - start_time
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logger.error(f"节点 {self.node_id} 执行超时（{timeout}秒）")
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@@ -217,7 +217,7 @@ class BaseNode(ABC):
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elapsed_time = time.time() - start_time
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logger.error(f"节点 {self.node_id} 执行失败: {e}", exc_info=True)
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return self._wrap_error(str(e), elapsed_time, state)
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async def run_stream(self, state: WorkflowState):
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"""Execute node with error handling and output wrapping (streaming)
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@@ -240,40 +240,41 @@ class BaseNode(ABC):
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State updates with streaming buffer and final result
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"""
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import time
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start_time = time.time()
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timeout = self.get_timeout()
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try:
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# Get LangGraph's stream writer for sending custom data
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writer = get_stream_writer()
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# Check if this is an End node
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# End nodes CAN send chunks (for suffix), but only after LLM content
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is_end_node = self.node_type == "end"
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# Check if this node is adjacent to End node (for message type)
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is_adjacent_to_end = getattr(self, '_is_adjacent_to_end', False)
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# Determine chunk type: "message" for End and adjacent nodes, "node_chunk" for others
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chunk_type = "message" if (is_end_node or is_adjacent_to_end) else "node_chunk"
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logger.debug(f"节点 {self.node_id} chunk 类型: {chunk_type} (is_end={is_end_node}, adjacent={is_adjacent_to_end})")
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logger.debug(
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f"节点 {self.node_id} chunk 类型: {chunk_type} (is_end={is_end_node}, adjacent={is_adjacent_to_end})")
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# Accumulate complete result (for final wrapping)
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chunks = []
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final_result = None
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chunk_count = 0
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# Stream chunks in real-time
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loop_start = asyncio.get_event_loop().time()
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async for item in self.execute_stream(state):
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# Check timeout
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if asyncio.get_event_loop().time() - loop_start > timeout:
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raise TimeoutError()
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# Check if it's a completion marker
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if isinstance(item, dict) and item.get("__final__"):
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final_result = item["result"]
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@@ -282,10 +283,10 @@ class BaseNode(ABC):
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chunk_count += 1
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chunks.append(item)
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full_content = "".join(chunks)
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# Send chunks for all nodes (including End nodes for suffix)
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logger.debug(f"节点 {self.node_id} 发送 chunk #{chunk_count}: {item[:50]}...")
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# 1. Send via stream writer (for real-time client updates)
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writer({
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"type": chunk_type, # "message" or "node_chunk"
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@@ -294,7 +295,7 @@ class BaseNode(ABC):
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"full_content": full_content,
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"chunk_index": chunk_count
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})
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# 2. Update streaming buffer in state (for downstream nodes)
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# Only non-End nodes need streaming buffer
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if not is_end_node:
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@@ -313,7 +314,7 @@ class BaseNode(ABC):
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chunk_str = str(item)
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chunks.append(chunk_str)
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full_content = "".join(chunks)
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# Send chunks for all nodes
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writer({
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"type": chunk_type, # "message" or "node_chunk"
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@@ -322,7 +323,7 @@ class BaseNode(ABC):
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"full_content": full_content,
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"chunk_index": chunk_count
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})
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# Only non-End nodes need streaming buffer
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if not is_end_node:
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yield {
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@@ -334,23 +335,23 @@ class BaseNode(ABC):
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}
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}
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}
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elapsed_time = time.time() - start_time
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logger.info(f"节点 {self.node_id} 流式执行完成，耗时: {elapsed_time:.2f}s, chunks: {chunk_count}")
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# Extract processed output (call subclass's _extract_output)
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extracted_output = self._extract_output(final_result)
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# Wrap final result
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final_output = self._wrap_output(final_result, elapsed_time, state)
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# Store extracted output in runtime variables (for quick access by subsequent nodes)
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if isinstance(extracted_output, dict):
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runtime_var = extracted_output
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else:
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runtime_var = {"output": extracted_output}
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# Build complete state update (including node_outputs, runtime_vars, and final streaming buffer)
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state_update = {
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**final_output,
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@@ -359,7 +360,7 @@ class BaseNode(ABC):
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},
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"looping": state["looping"]
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}
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# Add streaming buffer for non-End nodes
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if not is_end_node:
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state_update["streaming_buffer"] = {
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@@ -369,11 +370,11 @@ class BaseNode(ABC):
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"is_complete": True # Mark as complete
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}
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}
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# Finally yield state update
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# LangGraph will merge this into state
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yield state_update
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except TimeoutError:
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elapsed_time = time.time() - start_time
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logger.error(f"节点 {self.node_id} 执行超时 ({timeout}s)")
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@@ -384,12 +385,12 @@ class BaseNode(ABC):
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logger.error(f"节点 {self.node_id} 执行失败: {e}", exc_info=True)
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error_output = self._wrap_error(str(e), elapsed_time, state)
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yield error_output
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def _wrap_output(
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self,
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business_result: Any,
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elapsed_time: float,
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state: WorkflowState
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self,
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business_result: Any,
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elapsed_time: float,
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state: WorkflowState
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) -> dict[str, Any]:
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"""将业务结果包装成标准输出格式
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@@ -403,13 +404,13 @@ class BaseNode(ABC):
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"""
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# 提取输入数据（用于记录）
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input_data = self._extract_input(state)
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# 提取 token 使用情况（如果有）
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token_usage = self._extract_token_usage(business_result)
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# 提取实际输出（去除元数据）
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output = self._extract_output(business_result)
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# 构建标准节点输出
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node_output = {
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"node_id": self.node_id,
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@@ -422,18 +423,18 @@ class BaseNode(ABC):
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"token_usage": token_usage,
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"error": None
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}
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return {
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"node_outputs": {
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self.node_id: node_output
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}
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}
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def _wrap_error(
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self,
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error_message: str,
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elapsed_time: float,
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state: WorkflowState
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self,
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error_message: str,
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elapsed_time: float,
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state: WorkflowState
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) -> dict[str, Any]:
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"""将错误包装成标准输出格式
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@@ -447,10 +448,10 @@ class BaseNode(ABC):
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"""
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# 查找错误边
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error_edge = self._find_error_edge()
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# 提取输入数据
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input_data = self._extract_input(state)
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# 构建错误输出
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node_output = {
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"node_id": self.node_id,
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@@ -463,7 +464,7 @@ class BaseNode(ABC):
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"token_usage": None,
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"error": error_message
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}
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if error_edge:
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# 有错误边：记录错误并继续
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logger.warning(
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@@ -480,7 +481,7 @@ class BaseNode(ABC):
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# 无错误边：抛出异常停止工作流
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logger.error(f"节点 {self.node_id} 执行失败，停止工作流: {error_message}")
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raise Exception(f"节点 {self.node_id} 执行失败: {error_message}")
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def _extract_input(self, state: WorkflowState) -> dict[str, Any]:
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"""提取节点输入数据（用于记录）
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@@ -494,7 +495,7 @@ class BaseNode(ABC):
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"""
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# 默认返回配置
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return {"config": self.config}
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def _extract_output(self, business_result: Any) -> Any:
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"""从业务结果中提取实际输出
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@@ -508,7 +509,7 @@ class BaseNode(ABC):
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"""
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# 默认直接返回业务结果
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return business_result
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def _extract_token_usage(self, business_result: Any) -> dict[str, int] | None:
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"""从业务结果中提取 token 使用情况
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@@ -522,7 +523,7 @@ class BaseNode(ABC):
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"""
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# 默认返回 None
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return None
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def _find_error_edge(self) -> dict[str, Any] | None:
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"""查找错误边
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@@ -533,8 +534,8 @@ class BaseNode(ABC):
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if edge.get("source") == self.node_id and edge.get("type") == "error":
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return edge
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return None
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def _render_template(self, template: str, state: WorkflowState | None, struct: bool = True) -> str:
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def _render_template(self, template: str, state: WorkflowState | None, strict: bool = True) -> str:
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"""渲染模板
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支持的变量命名空间：
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@@ -550,28 +551,28 @@ class BaseNode(ABC):
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渲染后的字符串
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"""
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from app.core.workflow.template_renderer import render_template
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# 处理 state 为 None 的情况
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if state is None:
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state = {}
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# 使用变量池获取变量
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pool = VariablePool(state)
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# 构建完整的 variables 结构
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variables = {
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"sys": pool.get_all_system_vars(),
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"conv": pool.get_all_conversation_vars()
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}
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return render_template(
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template=template,
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variables=variables,
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node_outputs=pool.get_all_node_outputs(),
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system_vars=pool.get_all_system_vars(),
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struct=struct
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strict=strict
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)
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def _evaluate_condition(self, expression: str, state: WorkflowState | None) -> bool:
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"""评估条件表达式
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@@ -588,20 +589,20 @@ class BaseNode(ABC):
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布尔值结果
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"""
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from app.core.workflow.expression_evaluator import evaluate_condition
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# 处理 state 为 None 的情况
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if state is None:
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state = {}
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# 使用变量池获取变量
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pool = VariablePool(state)
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# 构建完整的 variables 结构（包含 sys 和 conv）
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variables = {
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"sys": pool.get_all_system_vars(),
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"conv": pool.get_all_conversation_vars()
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}
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return evaluate_condition(
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expression=expression,
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variables=variables,
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@@ -626,12 +627,12 @@ class BaseNode(ABC):
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>>> llm_output = pool.get("llm_qa.output")
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"""
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return VariablePool(state)
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def get_variable(
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self,
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selector: list[str] | str,
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state: WorkflowState,
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default: Any = None
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self,
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selector: list[str] | str,
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state: WorkflowState,
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default: Any = None
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) -> Any:
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"""获取变量值（便捷方法）
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@@ -650,7 +651,7 @@ class BaseNode(ABC):
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"""
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pool = VariablePool(state)
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return pool.get(selector, default=default)
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def has_variable(self, selector: list[str] | str, state: WorkflowState) -> bool:
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"""检查变量是否存在（便捷方法）
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@@ -37,7 +37,7 @@ class EndNode(BaseNode):
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# 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}'")
|
||||
|
||||
@@ -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 {}
|
||||
|
||||
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）
|
||||
}
|
||||
if self.strict:
|
||||
context = defaultdict(dict)
|
||||
context["conv"] = conv_vars
|
||||
context["nodes"] = node_outputs
|
||||
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)
|
||||
|
||||
|
||||
|
||||
@@ -53,7 +53,7 @@ nodes:
|
||||
type: end
|
||||
name: 结束
|
||||
config:
|
||||
output: "{{llm_qa.output}}"
|
||||
output: "{{ llm_qa.output }}"
|
||||
position:
|
||||
x: 900
|
||||
y: 100
|
||||
|
||||
Reference in New Issue
Block a user