defprocess_user_message(user_input, all_messages, debug=True): delimiter = "```" # Step 1: Check input to see if it flags the Moderation API or is a prompt injection response = openai.Moderation.create(input=user_input) moderation_output = response["results"][0]
if moderation_output["flagged"]: print("Step 1: Input flagged by Moderation API.") return"Sorry, we cannot process this request."
if debug: print("Step 1: Input passed moderation check.") category_and_product_response = utils.find_category_and_product_only(user_input, utils.get_products_and_category()) #print(print(category_and_product_response) # Step 2: Extract the list of products category_and_product_list = utils.read_string_to_list(category_and_product_response) #print(category_and_product_list)
if debug: print("Step 2: Extracted list of products.")
# Step 3: If products are found, look them up product_information = utils.generate_output_string(category_and_product_list) if debug: print("Step 3: Looked up product information.")
# Step 4: Answer the user question system_message = f""" You are a customer service assistant for a large electronic store. \ Respond in a friendly and helpful tone, with concise answers. \ Make sure to ask the user relevant follow-up questions. """ messages = [ {'role': 'system', 'content': system_message}, {'role': 'user', 'content': f"{delimiter}{user_input}{delimiter}"}, {'role': 'assistant', 'content': f"Relevant product information:\n{product_information}"} ]
final_response = get_completion_from_messages(all_messages + messages) if debug:print("Step 4: Generated response to user question.") all_messages = all_messages + messages[1:]
# Step 5: Put the answer through the Moderation API response = openai.Moderation.create(input=final_response) moderation_output = response["results"][0]
if moderation_output["flagged"]: if debug: print("Step 5: Response flagged by Moderation API.") return"Sorry, we cannot provide this information."
if debug: print("Step 5: Response passed moderation check.")
# Step 6: Ask the model if the response answers the initial user query well user_message = f""" Customer message: {delimiter}{user_input}{delimiter} Agent response: {delimiter}{final_response}{delimiter} Does the response sufficiently answer the question? """ messages = [ {'role': 'system', 'content': system_message}, {'role': 'user', 'content': user_message} ] evaluation_response = get_completion_from_messages(messages) if debug: print("Step 6: Model evaluated the response.")
# Step 7: If yes, use this answer; if not, say that you will connect the user to a human if"Y"in evaluation_response: # Using "in" instead of "==" to be safer for model output variation (e.g., "Y." or "Yes") if debug: print("Step 7: Model approved the response.") return final_response, all_messages else: if debug: print("Step 7: Model disapproved the response.") neg_str = "I'm unable to provide the information you're looking for. I'll connect you with a human representative for further assistance." return neg_str, all_messages
user_input = "tell me about the smartx pro phone and the fotosnap camera, the dslr one. Also what tell me about your tvs" response,_ = process_user_message(user_input,[]) print(response)
