[refactor] reorder LangGraph method definitions

workflow.py

@@ -64,72 +64,6 @@ class GAIAAnsweringWorkflow:
         answer = str(response.content)      
         return answer
 
-    @staticmethod
-    def default_qa_function(question: str) -> str:
-        """Placeholder QA function (override with your CodeAgent)"""
-        return "42"
-    
-    @staticmethod
-    def default_formatter(answer: str) -> str:
-        """Default GAIA formatting"""
-        return answer #f"\\boxed{{{answer}}}"
-    
-    def check_context_independent(self, state: AgentState)->bool:
-        if ctx := state.get("context"):
-            if ctx.get("filename"): 
-                return False
-        prompt = f"""
-        
-        I have a CodeAgent based on the text-to-text model that can use Internet search and parse the information found. 
-        If this approach is enough to successfully cope with the task, then we will call such a task an "easy question"
-
-        AS AN ERUDITE PERSON  YOU must analyze how difficult it will be to solve the next question 
-        <<{state["question"]}>>
-        
-        If you think that the question is easy, then return an empty string. Important! You should NOT add any symbols to the output in this case!
-        If the question concerns the use of additional resources such as complex analysis of downloaded files or resources on the Internet, then return an action plan
-
-        """
-        reply = self.ask_llm(prompt, True)
-        prompt = f""" The reasonings from other LLM is provided: <<{reply}>>
-        You have to Summarize: 
-        output either empty string ('') for easy question 
-        or extract action plan for non-easy question
-        """
-        reply = self.ask_llm(prompt, False)
-        if reply:
-            state["reasoning"].append(reply)
-            return False
-        return True         
-
-
-    def build_workflow(self) -> Any:
-        """Construct and compile the LangGraph workflow"""
-        # Create graph
-        workflow = StateGraph(AgentState)
-        
-        # Add nodes
-        workflow.add_node("preparations", self.preparations_node)
-        workflow.add_node("triage", self.triage_node)
-        workflow.add_node("deep_processing", self.deep_processing_node)
-        workflow.add_node("generate_answer", self.generate_answer_node)
-        workflow.add_node("format_output", self.format_output_node)
-        
-        # Define edges
-        workflow.set_entry_point("preparations")
-        workflow.add_edge("preparations", "triage")
-        workflow.add_conditional_edges("triage"
-                                        , self.check_context_independent
-                                        , {
-                                            True: "generate_answer",
-                                            False: "deep_processing"
-                                        })
-        workflow.add_edge("deep_processing", "format_output")
-
-        workflow.add_edge("generate_answer", "format_output")
-        workflow.add_edge("format_output", END)
-        
-        return workflow.compile()
 
     def extract_noted_urls_with_llm(self, question: str) -> List[str]:
         """Use LLM to extract URLs specifically noted in the question"""
@@ -177,6 +111,35 @@ class GAIAAnsweringWorkflow:
             print(f"File download failed: {str(e)}")
             return ""
 
+    
+    def check_context_independent(self, state: AgentState)->bool:
+        if ctx := state.get("context"):
+            if ctx.get("filename"): 
+                return False
+        prompt = f"""
+        
+        I have a CodeAgent based on the text-to-text model that can use Internet search and parse the information found. 
+        If this approach is enough to successfully cope with the task, then we will call such a task an "easy question"
+
+        AS AN ERUDITE PERSON  YOU must analyze how difficult it will be to solve the next question 
+        <<{state["question"]}>>
+        
+        If you think that the question is easy, then return an empty string. Important! You should NOT add any symbols to the output in this case!
+        If the question concerns the use of additional resources such as complex analysis of downloaded files or resources on the Internet, then return an action plan
+
+        """
+        reply = self.ask_llm(prompt, True)
+        prompt = f""" The reasonings from other LLM is provided: <<{reply}>>
+        You have to Summarize: 
+        output either empty string ('') for easy question 
+        or extract action plan for non-easy question
+        """
+        reply = self.ask_llm(prompt, False)
+        if reply:
+            state["reasoning"].append(reply)
+            return False
+        return True         
+
     def preparations_node(self, state: AgentState) -> dict:        
         if not state["context"]:
             return {}
@@ -260,6 +223,35 @@ Do NOT include << >> in your answer! Don't use full answer formulations! If you
         except Exception as e:
             return {"formatted_answer": f"\\boxed{{\\text{{Formatting error: {str(e)}}}}}"}
     
+    def build_workflow(self) -> Any:
+        """Construct and compile the LangGraph workflow"""
+        # Create graph
+        workflow = StateGraph(AgentState)
+        
+        # Add nodes
+        workflow.add_node("preparations", self.preparations_node)
+        workflow.add_node("triage", self.triage_node)
+        workflow.add_node("deep_processing", self.deep_processing_node)
+        workflow.add_node("generate_answer", self.generate_answer_node)
+        workflow.add_node("format_output", self.format_output_node)
+        
+        # Define edges
+        workflow.set_entry_point("preparations")
+        workflow.add_edge("preparations", "triage")
+        workflow.add_conditional_edges("triage"
+                                        , self.check_context_independent
+                                        , {
+                                            True: "generate_answer",
+                                            False: "deep_processing"
+                                        })
+        workflow.add_edge("deep_processing", "format_output")
+
+        workflow.add_edge("generate_answer", "format_output")
+        workflow.add_edge("format_output", END)
+        
+        return workflow.compile()
+
+
     
     def __call__(self, question: str, context: Dict|None=None) -> str:
         """
@@ -286,6 +278,16 @@ Do NOT include << >> in your answer! Don't use full answer formulations! If you
         result = self.workflow.invoke(initial_state)
         return result["formatted_answer"]
 
+    @staticmethod
+    def default_qa_function(question: str) -> str:
+        """Placeholder QA function (override with your CodeAgent)"""
+        return "42"
+    
+    @staticmethod
+    def default_formatter(answer: str) -> str:
+        """Default GAIA formatting"""
+        return answer #f"\\boxed{{{answer}}}"
+
 # Example usage with custom QA function
 if __name__ == "__main__":
     # Custom QA function (replace with your CodeAgent integration)