This puts together the chain of prompts that you saw throughout the course.
import os
import openai
import sys
sys.path.append('../..')
import utils
import panel as pn # GUI
pn.extension()
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
openai.api_key = os.environ['OPENAI_API_KEY']
def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0, max_tokens=500):
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
return response.choices[0].message["content"]
def process_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)
Step 1: Input passed moderation check. Step 2: Extracted list of products. Step 3: Looked up product information. Step 4: Generated response to user question. Step 5: Response passed moderation check. Step 6: Model evaluated the response. Step 7: Model approved the response. The SmartX ProPhone is a powerful smartphone with a 6.1-inch display, 128GB storage, 12MP dual camera, and 5G capabilities. The FotoSnap DSLR Camera is a versatile camera with a 24.2MP sensor, 1080p video, 3-inch LCD, and interchangeable lenses. As for our TVs, we have a range of options including the CineView 4K TV with a 55-inch display, 4K resolution, HDR, and smart TV capabilities, the CineView 8K TV with a 65-inch display, 8K resolution, HDR, and smart TV capabilities, and the CineView OLED TV with a 55-inch display, 4K resolution, HDR, and smart TV capabilities. Do you have any specific questions about these products or would you like me to recommend a product based on your needs?
def collect_messages(debug=False):
user_input = inp.value_input
if debug: print(f"User Input = {user_input}")
if user_input == "":
return
inp.value = ''
global context
#response, context = process_user_message(user_input, context, utils.get_products_and_category(),debug=True)
response, context = process_user_message(user_input, context, debug=False)
context.append({'role':'assistant', 'content':f"{response}"})
panels.append(
pn.Row('User:', pn.pane.Markdown(user_input, width=600)))
panels.append(
pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'})))
return pn.Column(*panels)
Note that the system message includes detailed instructions about what the OrderBot should do.
panels = [] # collect display
context = [ {'role':'system', 'content':"You are Service Assistant"} ]
inp = pn.widgets.TextInput( placeholder='Enter text hereā¦')
button_conversation = pn.widgets.Button(name="Service Assistant")
interactive_conversation = pn.bind(collect_messages, button_conversation)
dashboard = pn.Column(
inp,
pn.Row(button_conversation),
pn.panel(interactive_conversation, loading_indicator=True, height=300),
)
dashboard