Final

gemini_agent.py

@@ -339,66 +339,7 @@ class GeminiAgent:
         # Load answer bank
         self._load_answer_bank()
 
-    def _load_answer_bank(self):
-        """Load the answer bank from JSON file."""
-        try:
-            ans_bank_path = os.path.join(os.path.dirname(__file__), 'kndg_info.json')
-            with open(ans_bank_path, 'r') as f:
-                self.answer_bank = json.load(f)
-        except Exception as e:
-            print(f"Warning: Could not load answer bank: {e}")
-            self.answer_bank = []
-
-    def _check_answer_bank(self, query: str) -> Optional[str]:
-        """Check if query matches any question in answer bank using LLM with retries."""
-        max_retries = 5
-        base_sleep = 1
-        
-        for attempt in range(max_retries):
-            try:
-                if not self.answer_bank:
-                    return None
-
-                # Filter questions with answer_score = 1
-                valid_questions = [entry for entry in self.answer_bank if entry.get('answer_score', 0) == 1]
-                if not valid_questions:
-                    return None
-
-                # Create a prompt for the LLM to compare the query with answer bank questions
-                prompt = f"""Given a user query and a list of reference questions, determine if the query is semantically similar to any of the reference questions.
-Consider them similar if they are asking for the same information, even if phrased differently.
-
-User Query: {query}
-
-Reference Questions:
-{json.dumps([{'id': i, 'question': q['question']} for i, q in enumerate(valid_questions)], indent=2)}
-
-Instructions:
-1. Compare the user query with each reference question
-2. If there is a semantically similar match (asking for the same information), return the ID of the matching question
-3. If no good match is found, return -1
-4. Provide ONLY the number (ID or -1) as response, no other text
-
-Response:"""
-
-                messages = [HumanMessage(content=prompt)]
-                response = self.llm.invoke(messages)
-                match_id = int(response.content.strip())
-                
-                if match_id >= 0 and match_id < len(valid_questions):
-                    print(f"Hmm! Match found for query: {query}")
-                    return valid_questions[match_id]['answer']
-                    
-                return None
-
-            except Exception as e:
-                sleep_time = base_sleep * (attempt + 1)
-                if attempt < max_retries - 1:
-                    print(f"Answer bank check attempt {attempt + 1} failed. Retrying in {sleep_time} seconds...")
-                    time.sleep(sleep_time)
-                    continue
-                print(f"Warning: Error in answer bank check after {max_retries} attempts: {e}")
-                return None
+    
 
     def run(self, query: str) -> str:
         """Run the agent on a query with incremental retries."""
@@ -407,10 +348,6 @@ Response:"""
         
         for attempt in range(max_retries):
             try:
-                # First check answer bank
-                cached_answer = self._check_answer_bank(query)
-                if cached_answer:
-                    return cached_answer
 
                 # If no match found in answer bank, use the agent
                 response = self.agent.run(query)