Logging

researchsimulation/InteractiveInterviewChatbot.py

@@ -16,7 +16,9 @@ def parse_question_with_llm(question, respondent_names, processor_llm):
     Uses OpenAI's LLM to extract the specific agents being addressed and their respective questions.
     Supports compound requests.
     """
-    logging.info(f"Parsing question with LLM: {question}")
+    logging.info("🔍 ENTERING: parse_question_with_llm()")
+    logging.info(f"Received user input: {question}")
+    logging.info(f"Available respondent names: {respondent_names}")
     
     prompt = f"""
     You are an expert in market research interview analysis.
@@ -61,12 +63,24 @@ def parse_question_with_llm(question, respondent_names, processor_llm):
     
     Only return the formatted output without explanations.
     """
+    
+    logging.info("Prompt constructed. Invoking LLM now...")
+
+    try:
+        response = processor_llm.invoke(prompt)
+        if not hasattr(response, "content") or not response.content:
+            logging.error("LLM response is empty or malformed.")
+            return {}
+
+        chatgpt_output = response.content.strip()
+        logging.info(f"Raw LLM Output:\n{chatgpt_output}")
+
+    except Exception as e:
+        logging.exception(f"Exception occurred during LLM invocation in parse_question_with_llm: {e}")
+        return {}
 
-    # Invoke LangChain LLM
-    logging.info("Invoking LLM for parsing...")
-    response = processor_llm.invoke(prompt)
-    chatgpt_output = response.content.strip()
-    logging.info(f"LLM Parsed Output: {chatgpt_output}")
+    # Begin parsing the structured response
+    logging.info("Parsing LLM output for respondent-question pairs...")
     
     parsed_questions = {}
     respondent_name = "General"
@@ -83,7 +97,11 @@ def parse_question_with_llm(question, respondent_names, processor_llm):
                 respondent_name = "General"
                 question_text = None
     
-    logging.info("Parsing complete.")
+    if not parsed_questions:
+        logging.warning("No respondent-question pairs were successfully parsed.")
+
+    logging.info(f"Final parsed questions: {parsed_questions}")
+    logging.info("Exiting parse_question_with_llm()")
     return parsed_questions
 
 def validate_question_topics(parsed_questions, processor_llm):
@@ -92,7 +110,9 @@ def validate_question_topics(parsed_questions, processor_llm):
     Converts question to British English spelling if valid.
     Returns 'INVALID' for any out-of-scope question.
     """
-    logging.info("Validating question topics and converting to British English...")
+    logging.info("ENTERING: validate_question_topics()")
+    logging.info("Starting question validation against permitted scope...")
+    
     validated_questions = {}
 
     for respondent, question in parsed_questions.items():
@@ -163,12 +183,25 @@ def validate_question_topics(parsed_questions, processor_llm):
         # ### Output:
         # <Validated question in British English, or "INVALID">
         
-        response = processor_llm.invoke(prompt)
-        validated_output = response.content.strip()
-        logging.info(f"Validated output for {respondent}: {validated_output}")
+        try:
+            logging.debug("Sending validation prompt to LLM...")
+            response = processor_llm.invoke(prompt)
+
+            if not hasattr(response, "content") or not response.content:
+                logging.error(f"Empty or malformed response from LLM for respondent: '{respondent}'")
+                validated_output = "INVALID"
+            else:
+                validated_output = response.content.strip()
+                logging.info(f"Validation output for '{respondent}': {validated_output}")
+
+        except Exception as e:
+            logging.exception(f"Exception during validation for respondent '{respondent}': {e}")
+            validated_output = "INVALID"
+
         validated_questions[respondent] = validated_output
 
-    logging.info("Validation complete.")
+    logging.info("Completed validation for all questions.")
+    logging.debug(f"Final validated questions dictionary:\n{validated_questions}")
     return validated_questions
 
 
@@ -180,7 +213,8 @@ def ask_interview_question(respondent_agents_dict, last_active_agent, question,
     Uses Groq's LLM for response generation.
     """
 
-    logging.info(f"Received question: {question}")
+    logging.info(f"START: Processing new interview question: {question}")
+    responses = []
 
     agent_names = list(respondent_agents_dict.keys())
     logging.info(f"Available respondents: {agent_names}")
@@ -189,13 +223,19 @@ def ask_interview_question(respondent_agents_dict, last_active_agent, question,
     # Use OpenAI LLM to parse questions into individual respondent-specific sub-questions and validate them
     
     # Step 1: Parse question
+    logging.info("STEP 1: Parsing question with LLM...")
     parsed_questions = parse_question_with_llm(question, str(agent_names), processor_llm)
+    logging.info(f"Parsed Questions Output: {parsed_questions}")
+    
     if not parsed_questions:
         logging.warning("No questions were parsed from input.")
         return ["**PreData Moderator**: No valid respondents were detected for this question."]
 
     # Step 2: Validate question content (scope + spelling)
+    logging.info("STEP 2: Validating questions for topic relevance and British English...")
     validated_questions = validate_question_topics(parsed_questions, processor_llm)
+    logging.info(f"Validated Questions: {validated_questions}")
+    
     for resp_name, extracted_question in validated_questions.items():
         if extracted_question == "INVALID":
             logging.warning(f"Invalid question detected for {resp_name}: {extracted_question}")