tokencost/README.md

TokenCost

Overview

TokenCost is a specialized tool designed for calculating the token count and associated U.S. dollar cost of strings and messages used in Large Language Models (LLMs). This utility is particularly useful for developers and researchers working with language models, enabling them to estimate the computational resources required for processing various inputs and their returned outputs.

Features

• Token Counting: Accurately counts the number of tokens in a given string or message.
• Cost Calculation: Computes the cost of processing based on the token count, considering the specific pricing model of the LLM in use.
• Support for Multiple LLMs: Compatible with various Large Language Models.
• Easy Integration: Simple API for integrating with existing projects or workflows.

Installation

Tokencost can be installed either via PyPI or GitHub.

With PyPI (Python package):

pip install tokencost

With GitHub:

git clone git@github.com:AgentOps-AI/tokencost.git
cd tokencost
pip install -e .

Usage

To use TokenCost, follow these steps:

1. Import the module:

• If you want to call the functions as function_name directly:

from tokencost import *

• OR if you want to call the functions as tokencost.function_name:

import tokencost

2. Calculate tokens and cost (using from tokencost import *):

string_prompt = "Your sample text here"
response = "Sample response text"
model= "gpt-3.5-turbo"

string_cost = calculate_cost(string_prompt, response, model)

prompt_string_token_count = count_string_tokens(string_prompt, model)

print(f"Prompt Token Count: {prompt_string_token_count}, Completion Token Count:{completion_string_token_count}, Cost: ${string_cost/USD_PER_TPU} ({string_cost/CENTS_PER_TPU} cents)")

messages =[
    {
        "role": "user",
        "content": "Hey how is your day",
    },
    {
        "role": 'assistant',
        "content": "As an LLM model I do not have days"
    },
    {
        "role": "user",
        "content": "Err sure okay fine"
    }
]
response = "Sample response text"
model= "gpt-3.5-turbo"

message_cost = calculate_cost(messages, response, model)

prompt_message_token_count = count_message_tokens(messages, model)
completion_string_token_count = count_string_tokens(response, model)

print(f"Prompt Token Count: {prompt_message_token_count}, Completion Token Count: {completion_string_token_count}, Cost: ${message_cost/USD_PER_TPU} ({message_cost/CENTS_PER_TPU} cents)")

This is what it should look like when you use iPython:

In [1]: from tokencost import *

In [2]: prompt = "Your sample text here"
   ...: response = "Sample response text"
   ...: model= "gpt-3.5-turbo"
   ...: prompt_token_count = count_string_tokens(prompt, model)
   ...: completion_token_count =count_string_tokens(response, model)
   ...: cost = calculate_cost(prompt, response, model)
   ...:
   ...:
   ...: print(f"Prompt Token Count: {prompt_token_count}, Completion Token Count: {c
   ...: ompletion_token_count}, Cost: ${cost/USD_PER_TPU} ({cost/CENTS_PER_TPU} cent
   ...: s)")
Prompt Token Count: 4, Completion Token Count: 3, Cost: $1.2e-05 (0.0012 cents)

Running tests

0. Install pytest if you don't have it already

pip install pytest

1. Run the tests/ folder while in the parent directory

pytest tests

Contributing

Contributions to TokenCost are welcome! Feel free to create an issue for any bug reports, complaints, or feature suggestions.

License

TokenCost is released under the MIT License.