Update agent.py

agent.py

@@ -11,7 +11,6 @@ from dotenv import load_dotenv
 from langgraph.graph import StateGraph, END
 from langchain.tools import Tool as LangTool
 from langchain_core.runnables import RunnableLambda
-from pathlib import Path
 
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from langchain_community.llms import HuggingFacePipeline
@@ -185,7 +184,7 @@ def analyze_question(state: AgentState) -> AgentState:
     """
     
     try:
-        response = llm.invoke(analysis_prompt).content
+        response = llm.invoke(analysis_prompt)
         state["conversation_history"].append({"role": "analysis", "content": response})
         state["current_step"] = AgentStep.SELECT_TOOLS.value
     except Exception as e:
@@ -264,7 +263,7 @@ def select_tools(state: AgentState) -> AgentState:
     A:
     """
     
-    llm_tools = ast.literal_eval(llm.invoke(tools_prompt).content.strip())
+    llm_tools = ast.literal_eval(llm.invoke(tools_prompt).strip())
     if not isinstance(llm_tools, list):
         llm_tools = []
     print(f"LLM suggested tools: {llm_tools}")
@@ -378,7 +377,7 @@ def synthesize_answer(state: AgentState) -> AgentState:
 
     Your step-by-step analysis:"""
 
-    cot_response = llm.invoke(cot_prompt).content
+    cot_response = llm.invoke(cot_prompt)
 
     final_answer_prompt = f"""You are a precise assistant tasked with deriving the **final answer** from the step-by-step analysis below.
 
@@ -399,7 +398,7 @@ def synthesize_answer(state: AgentState) -> AgentState:
 
         
     try:
-        response = llm.invoke(final_answer_prompt).content
+        response = llm.invoke(final_answer_prompt)
         state["final_answer"] = response
         state["current_step"] = AgentStep.COMPLETE.value
     except Exception as e:
@@ -424,7 +423,7 @@ def error_recovery(state: AgentState) -> AgentState:
             Provide a helpful response even if you cannot access external tools.
             Be clear about any limitations in your answer.
             """
-            response = llm.invoke(fallback_prompt).content
+            response = llm.invoke(fallback_prompt)
             state["final_answer"] = f"Using available knowledge (some tools unavailable): {response}"
             state["current_step"] = AgentStep.COMPLETE.value
         except Exception as e:
@@ -543,7 +542,7 @@ class GaiaAgent:
             
             # Try fallback direct LLM response
             try:
-                fallback_response = llm.invoke(f"Please answer this question: {question}").content
+                fallback_response = llm.invoke(f"Please answer this question: {question}")
                 return f"Fallback response: {fallback_response}"
             except:
                 return error_msg