Evaluate Inputs: Classification

Setup

Load the API key and relevant Python libaries.

Some code that loads the OpenAI API key for you.

import openai
import os

from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())

openai.api_type = os.getenv("api_type")
openai.api_base = os.getenv("api_base")
openai.api_version = os.getenv("api_version")
openai.api_key = os.getenv("OPENAI_API_KEY")

def get_completion_from_messages(messages, 
                                 model="chatgpt-gpt35-turbo", 
                                 temperature=0, 
                                 max_tokens=500):
    response = openai.ChatCompletion.create(
        engine=model,
        messages=messages,
        temperature=temperature, 
        max_tokens=max_tokens,
    )
    return response.choices[0].message["content"]

Classify customer queries to handle different cases

delimiter = "####"
system_message = f"""
You will be provided with customer service queries. \
The customer service query will be delimited with \
{delimiter} characters.
Classify each query into a primary category \
and a secondary category. 
Provide your output in json format with the \
keys: primary and secondary.

Primary categories: Billing, Technical Support, \
Account Management, or General Inquiry.

Billing secondary categories:
Unsubscribe or upgrade
Add a payment method
Explanation for charge
Dispute a charge

Technical Support secondary categories:
General troubleshooting
Device compatibility
Software updates

Account Management secondary categories:
Password reset
Update personal information
Close account
Account security

General Inquiry secondary categories:
Product information
Pricing
Feedback
Speak to a human

"""
user_message = f"""\
I want you to delete my profile and all of my user data"""
messages =  [  
{'role':'system', 
 'content': system_message},    
{'role':'user', 
 'content': f"{delimiter}{user_message}{delimiter}"},  
] 
response = get_completion_from_messages(messages)
print(response)

{
  "primary": "Account Management",
  "secondary": "Close account"
}

user_message = f"""\
Tell me more about your flat screen tvs"""
messages =  [  
{'role':'system', 
 'content': system_message},    
{'role':'user', 
 'content': f"{delimiter}{user_message}{delimiter}"},  
] 
response = get_completion_from_messages(messages)
print(response)

{
  "primary": "General Inquiry",
  "secondary": "Product information"
}