perf(workflow): add tests, adapt some LLM node output formats, optimize sandbox return format

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

@@ -18,6 +18,8 @@ from app.core.workflow.nodes.start import StartNode
 from app.core.workflow.nodes.parameter_extractor import ParameterExtractorNode
 from app.core.workflow.nodes.question_classifier import QuestionClassifierNode
 from app.core.workflow.nodes.tool import ToolNode
+from app.core.workflow.nodes.variable_aggregator import VariableAggregatorNode
+from app.core.workflow.nodes.code import CodeNode
 
 __all__ = [
     "BaseNode",
@@ -35,5 +37,7 @@ __all__ = [
     "JinjaRenderNode",
     "ParameterExtractorNode",
     "QuestionClassifierNode",
-    "ToolNode"
+    "ToolNode",
+    "CodeNode",
+    "VariableAggregatorNode"
 ]

api/app/core/workflow/nodes/code/__init__.py

@@ -1,3 +1,4 @@
+from app.core.workflow.nodes.code.config import CodeNodeConfig
 from app.core.workflow.nodes.code.node import CodeNode
 
-__all__ = ["CodeNode"]
+__all__ = ["CodeNode", "CodeNodeConfig"]

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

@@ -216,7 +216,7 @@ class LLMNode(BaseNode):
         logger.info(f"节点 {self.node_id} LLM 调用完成，输出长度: {len(content)}")
 
         # 返回 AIMessage（包含响应元数据）
-        return response if isinstance(response, AIMessage) else AIMessage(content=content)
+        return AIMessage(content=content, response_metadata=response.response_metadata)
 
     def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]:
         """提取输入数据（用于记录）"""

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

@@ -193,7 +193,8 @@ class ParameterExtractorNode(BaseNode):
 
         model_resp = await llm.ainvoke(messages)
         self.response_metadata = model_resp.response_metadata
-        result = json_repair.repair_json(model_resp.content, return_objects=True)
+        model_message = self.process_model_output(model_resp.content)
+        result = json_repair.repair_json(model_message, return_objects=True)
         logger.info(f"node: {self.node_id} get params:{result}")
 
         return result

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

@@ -131,7 +131,7 @@ class QuestionClassifierNode(BaseNode):
             ]
 
             response = await llm.ainvoke(messages)
-            result = response.content.strip()
+            result = self.process_model_output(response.content)
             self.response_metadata = response.response_metadata
 
             if result in category_names: