Update app.py

app.py

@@ -137,16 +137,21 @@ class BasicAgent:
 
     # ---- Low‑level LLM call
     def _call_llm(self, prompt: str, max_tokens: int = 256) -> str:
-        resp = self.llm.chat.completions.create(
-            model="gpt-4o-mini",
-            messages=[
-                {"role": "system", "content": "You are a careful reasoning assistant."},
-                {"role": "user", "content": prompt},
-            ],
-            temperature=0.3,
-            max_tokens=max_tokens,
-        )
-        return resp.choices[0].message.content.strip()
+        try:
+            resp = self.llm.chat.completions.create(
+                model="gpt-4o-mini",
+                messages=[
+                    {"role": "system", "content": "You are a careful reasoning assistant."},
+                    {"role": "user", "content": prompt},
+                ],
+                temperature=0.3,
+                max_tokens=max_tokens,
+            )
+            return resp.choices[0].message.content.strip()
+        except Exception as e:
+            print(f"\nLLM Error: {str(e)}")
+            print(f"Prompt that caused error:\n{prompt}")
+            raise
 
     # ---- Workflow nodes
     def _analyze_question(self, state: AgentState) -> AgentState:
@@ -184,7 +189,7 @@ class BasicAgent:
         except (json.JSONDecodeError, KeyError):
             pass  # Not a JSON question or no file_url
         except Exception as e:
-            state["logs"]["file_download_error"] = str(e)
+            print(f"\nFile handling error: {str(e)}")
             state["current_step"] = "answer"
             return state
         
@@ -195,18 +200,18 @@ class BasicAgent:
             "{\n  'needs_search': bool,\n  'search_query': str\n} \n\n"
             f"Question: {state['question']}"
         )
-        raw = self._call_llm(prompt)
         try:
+            raw = self._call_llm(prompt)
             decision = ast.literal_eval(raw)
             state["needs_search"] = bool(decision.get("needs_search", False))
             state["search_query"] = decision.get("search_query", state["question"])
-        except Exception:
+        except Exception as e:
+            print(f"\nLLM Error in question analysis: {str(e)}")
+            print(f"Raw response: {raw}")
             state["needs_search"] = True
             state["search_query"] = state["question"]
             decision = {"parse_error": raw}
-        state["logs"] = {
-            "analyze": {"prompt": prompt, "llm_response": raw, "decision": decision}
-        }
+            
         state["current_step"] = "search" if state["needs_search"] else "answer"
         state["history"].append({"step": "analyze", "output": decision})
         return state
@@ -366,19 +371,35 @@ MATERIALS:
 
 Think step-by-step. Write ANSWER: <answer> on its own line.
 """
-        raw = self._call_llm(prompt, 300)
-        answer = raw.split("ANSWER:")[-1].strip()
-        
-        # Validate answer
-        if not answer:
-            answer = "I cannot provide a definitive answer at this time."
-        elif any(k in answer.lower() for k in ["i cannot find", "sorry"]):
-            # Fall back to a more general response
-            answer = "Based on the available information, I cannot provide a complete answer."
-        
-        state["final_answer"] = answer
-        state["history"].append({"step": "answer", "output": raw})
-        state["current_step"] = "done"
+        try:
+            raw = self._call_llm(prompt, 300)
+            answer = raw.split("ANSWER:")[-1].strip()
+            
+            # Validate answer
+            if not answer:
+                print("\nLLM Warning: Empty answer received")
+                print(f"Raw response: {raw}")
+                answer = "I cannot provide a definitive answer at this time."
+            elif any(k in answer.lower() for k in ["i cannot find", "sorry"]):
+                print("\nLLM Warning: LLM indicated it couldn't find an answer")
+                print(f"Raw response: {raw}")
+                answer = "Based on the available information, I cannot provide a complete answer."
+            elif "ANSWER:" not in raw:
+                print("\nLLM Warning: Response missing ANSWER: prefix")
+                print(f"Raw response: {raw}")
+                answer = "I cannot provide a definitive answer at this time."
+            
+            state["final_answer"] = answer
+            state["history"].append({"step": "answer", "output": raw})
+            state["current_step"] = "done"
+            
+        except Exception as e:
+            print(f"\nLLM Error in answer generation: {str(e)}")
+            print(f"Question: {state['question']}")
+            print(f"Materials:\n{search_block}")
+            state["final_answer"] = "I encountered an error while generating the answer."
+            state["current_step"] = "done"
+            
         return state
 
     # ---- Build LangGraph workflow