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testTurbo35.py
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testTurbo35.py
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# import os module & the OpenAI Python library for calling the OpenAI API
# please make sure you have installed required libraries via pip install -r requirements.txt
#import os
import openai
import json
import tiktoken
# Load config values
import config as cfg
#initializing openAI API with your API key
openai.api_base = cfg.api_base
openai.api_key = cfg.azure_openai_key #cfg.openai_key
engine_name= cfg.deployment_name
#only azure api need below two lines
openai.api_type = cfg.api_type
openai.api_version = cfg.api_version
max_response_tokens = 1000
#Create the system message for ChatGPT
base_system_message = "Assistant is a large language model trained by OpenAI." #"You are a startup coach that helps entrepreneurs design their business."
system_message = f"{base_system_message.strip()}"
print(system_message)
#Define helper functions
def num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301"):
encoding = tiktoken.encoding_for_model(model)
num_tokens = 0
for message in messages:
num_tokens += 4 # every message follows <im_start>{role/name}\n{content}<im_end>\n
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name": # if there's a name, the role is omitted
num_tokens += -1 # role is always required and always 1 token
num_tokens += 2 # every reply is primed with <im_start>assistant
return num_tokens
# Defining a function to send the prompt to the ChatGPT model
# More info : https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/chatgpt?pivots=programming-language-chat-completions
def send_message(messages, chatgpt_model_name, max_response_tokens=500):
response = openai.ChatCompletion.create(
engine=chatgpt_model_name,
messages=messages,
temperature=0,
max_tokens=max_response_tokens,
top_p=0.9,
frequency_penalty=0,
presence_penalty=0,
)
return response['choices'][0]['message']['content']
# Defining a function to print out the conversation in a readable format
def print_conversation(messages):
for message in messages:
print(f"[{message['role'].upper()}]")
print(message['content'])
print()
# Start the conversation
#user_message = input("🔥\'sup, bro?🔥")
# Create the list of messages. role can be either "user" or "assistant"
messages=[
{"role": "system", "content": system_message}
#,{"role": "user", "name":"example_user", "content": user_message}
]
#Keep the conversation within a given token limit
overall_max_tokens = 2000#4096
prompt_max_tokens = overall_max_tokens - max_response_tokens
user_message=""
"""
token_count = num_tokens_from_messages(messages)
print(f"Token count0: {token_count}")
response = send_message(messages, engine_name, max_response_tokens)
messages.append({"role": "assistant", "content": response})
print_conversation(messages)
#user_message = "The target audience for the blog post should be business leaders working in the tech industry."
#user_message = "Let's talk about generative AI and keep the tone informational but also friendly."
#user_message = "Show me a few more examples"
#messages.append({"role": "user", "content": user_message})
"""
token_count = num_tokens_from_messages(messages)
print(f"Token count0: {token_count}")
print(f"init msg: {messages}")
# remove first message while over the token limit
while user_message not in ["quit", "exit"]:
#print(f"Token count1: {token_count}")
#messages.pop(0)
#print(f"Token count2: {token_count}")
#print(f"current msg: {messages}")
user_message = input("🔥One more try?🔥")
messages.append({"role": "user", "content": user_message})
token_count = num_tokens_from_messages(messages)
if token_count>prompt_max_tokens:
messages.pop(0)
#print(f"Token count3: {token_count}")
response = send_message(messages, engine_name, max_response_tokens)
messages.append({"role": "assistant", "content": response})
prompt_max_tokens-= token_count # reduce the prompt length as needed.
#print(f"prompt_max_tokens: {prompt_max_tokens}")
print_conversation(messages)
#print(f"Token count4: {token_count}")