How_to_format_inputs_to_ChatGPT_models.py

# import the OpenAI Python library for calling the OpenAI API
import openai

def open_file(filepath):
    with open(filepath, 'r', encoding='utf-8') as infile:
        return infile.read()

openai.api_key = open_file('openaiapikey.txt')

# Example OpenAI Python library request
MODEL = "gpt-3.5-turbo"
response = openai.ChatCompletion.create(
    model=MODEL,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Knock knock."},
        {"role": "assistant", "content": "Who's there?"},
        {"role": "user", "content": "Orange."},
    ],
    temperature=0,
)

response

response['choices'][0]['message']['content']

# example with a system message
response = openai.ChatCompletion.create(
    model=MODEL,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain asynchronous programming in the style of the pirate Blackbeard."},
    ],
    temperature=0,
)

print(response['choices'][0]['message']['content'])

# example without a system message
response = openai.ChatCompletion.create(
    model=MODEL,
    messages=[
        {"role": "user", "content": "Explain asynchronous programming in the style of the pirate Blackbeard."},
    ],
    temperature=0,
)

print(response['choices'][0]['message']['content'])

# An example of a system message that primes the assistant to explain concepts in great depth
response = openai.ChatCompletion.create(
    model=MODEL,
    messages=[
        {"role": "system", "content": "You are a friendly and helpful teaching assistant. You explain concepts in great depth using simple terms, and you give examples to help people learn. At the end of each explanation, you ask a question to check for understanding"},
        {"role": "user", "content": "Can you explain how fractions work?"},
    ],
    temperature=0,
)

print(response["choices"][0]["message"]["content"])

# An example of a system message that primes the assistant to give brief, to-the-point answers
response = openai.ChatCompletion.create(
    model=MODEL,
    messages=[
        {"role": "system", "content": "You are a laconic assistant. You reply with brief, to-the-point answers with no elaboration."},
        {"role": "user", "content": "Can you explain how fractions work?"},
    ],
    temperature=0,
)

print(response["choices"][0]["message"]["content"])

# An example of a faked few-shot conversation to prime the model into translating business jargon to simpler speech
response = openai.ChatCompletion.create(
    model=MODEL,
    messages=[
        {"role": "system", "content": "You are a helpful, pattern-following assistant."},
        {"role": "user", "content": "Help me translate the following corporate jargon into plain English."},
        {"role": "assistant", "content": "Sure, I'd be happy to!"},
        {"role": "user", "content": "New synergies will help drive top-line growth."},
        {"role": "assistant", "content": "Things working well together will increase revenue."},
        {"role": "user", "content": "Let's circle back when we have more bandwidth to touch base on opportunities for increased leverage."},
        {"role": "assistant", "content": "Let's talk later when we're less busy about how to do better."},
        {"role": "user", "content": "This late pivot means we don't have time to boil the ocean for the client deliverable."},
    ],
    temperature=0,
)

print(response["choices"][0]["message"]["content"])

# The business jargon translation example, but with example names for the example messages
response = openai.ChatCompletion.create(
    model=MODEL,
    messages=[
        {"role": "system", "content": "You are a helpful, pattern-following assistant that translates corporate jargon into plain English."},
        {"role": "system", "name":"example_user", "content": "New synergies will help drive top-line growth."},
        {"role": "system", "name": "example_assistant", "content": "Things working well together will increase revenue."},
        {"role": "system", "name":"example_user", "content": "Let's circle back when we have more bandwidth to touch base on opportunities for increased leverage."},
        {"role": "system", "name": "example_assistant", "content": "Let's talk later when we're less busy about how to do better."},
        {"role": "user", "content": "This late pivot means we don't have time to boil the ocean for the client deliverable."},
    ],
    temperature=0,
)

print(response["choices"][0]["message"]["content"])

import tiktoken


def num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301"):
    """Returns the number of tokens used by a list of messages."""
    try:
        encoding = tiktoken.encoding_for_model(model)
    except KeyError:
        encoding = tiktoken.get_encoding("cl100k_base")
    if model == "gpt-3.5-turbo-0301":  # note: future models may deviate from this
        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
    else:
        raise NotImplementedError(f"""num_tokens_from_messages() is not presently implemented for model {model}.
See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""")

messages = [
    {"role": "system", "content": "You are a helpful, pattern-following assistant that translates corporate jargon into plain English."},
    {"role": "system", "name":"example_user", "content": "New synergies will help drive top-line growth."},
    {"role": "system", "name": "example_assistant", "content": "Things working well together will increase revenue."},
    {"role": "system", "name":"example_user", "content": "Let's circle back when we have more bandwidth to touch base on opportunities for increased leverage."},
    {"role": "system", "name": "example_assistant", "content": "Let's talk later when we're less busy about how to do better."},
    {"role": "user", "content": "This late pivot means we don't have time to boil the ocean for the client deliverable."},
]

# example token count from the function defined above
print(f"{num_tokens_from_messages(messages)} prompt tokens counted.")