How to count tokens with tiktoken

%pip install --upgrade tiktoken

import tiktoken

encoding = tiktoken.get_encoding("cl100k_base")

encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")

encoding.encode("tiktoken is great!")

def num_tokens_from_string(string: str, encoding_name: str) -> int:
    """Returns the number of tokens in a text string."""
    encoding = tiktoken.get_encoding(encoding_name)
    num_tokens = len(encoding.encode(string))
    return num_tokens

num_tokens_from_string("tiktoken is great!", "cl100k_base")

encoding.decode([83, 1609, 5963, 374, 2294, 0])

[encoding.decode_single_token_bytes(token) for token in [83, 1609, 5963, 374, 2294, 0]]

def compare_encodings(example_string: str) -> None:
    """Prints a comparison of three string encodings."""
    # print the example string
    print(f'\nExample string: "{example_string}"')
    # for each encoding, print the # of tokens, the token integers, and the token bytes
    for encoding_name in ["gpt2", "p50k_base", "cl100k_base"]:
        encoding = tiktoken.get_encoding(encoding_name)
        token_integers = encoding.encode(example_string)
        num_tokens = len(token_integers)
        token_bytes = [encoding.decode_single_token_bytes(token) for token in token_integers]
        print()
        print(f"{encoding_name}: {num_tokens} tokens")
        print(f"token integers: {token_integers}")
        print(f"token bytes: {token_bytes}")
        

compare_encodings("antidisestablishmentarianism")

compare_encodings("2 + 2 = 4")

compare_encodings("お誕生日おめでとう")

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:
        print("Warning: model not found. Using cl100k_base encoding.")
        encoding = tiktoken.get_encoding("cl100k_base")
    if model == "gpt-3.5-turbo":
        print("Warning: gpt-3.5-turbo may change over time. Returning num tokens assuming gpt-3.5-turbo-0301.")
        return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301")
    elif model == "gpt-4":
        print("Warning: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
        return num_tokens_from_messages(messages, model="gpt-4-0314")
    elif model == "gpt-3.5-turbo-0301":
        tokens_per_message = 4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
        tokens_per_name = -1  # if there's a name, the role is omitted
    elif model == "gpt-4-0314":
        tokens_per_message = 3
        tokens_per_name = 1
    else:
        raise NotImplementedError(f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""")
    num_tokens = 0
    for message in messages:
        num_tokens += tokens_per_message
        for key, value in message.items():
            num_tokens += len(encoding.encode(value))
            if key == "name":
                num_tokens += tokens_per_name
    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
    return num_tokens

# let's verify the function above matches the OpenAI API response

import openai

example_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.",
    },
]

for model in ["gpt-3.5-turbo-0301", "gpt-4-0314"]:
    print(model)
    # example token count from the function defined above
    print(f"{num_tokens_from_messages(example_messages, model)} prompt tokens counted by num_tokens_from_messages().")
    # example token count from the OpenAI API
    response = openai.ChatCompletion.create(
        model=model,
        messages=example_messages,
        temperature=0,
        max_tokens=1  # we're only counting input tokens here, so let's not waste tokens on the output
    )
    print(f'{response["usage"]["prompt_tokens"]} prompt tokens counted by the OpenAI API.')
    print()