Metadata-Version: 2.1
Name: tokencal
Version: 0.1.2
Summary: A powerful package for estimating token prices in large language models.
Author-email: fardinkai <fardin.opai@gmail.com>
Project-URL: Homepage, https://github.com/FardinHash/tokencal
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: dev
Requires-Dist: pytest>=7.4.4; extra == "dev"
Requires-Dist: flake8>=3.1.0; extra == "dev"
Requires-Dist: coverage[toml]>=7.4.0; extra == "dev"

# Tokencal: Token Price Estimation for LLMs
<p align="center">
  <img src="tokencal.png" height="500" alt="Tokencal" />
</p>

<p align="center">
    <a href="https://pypi.org/project/tokencal/" target="_blank">
        <img alt="Version" src="https://img.shields.io/pypi/v/tokencal?style=for-the-badge&color=3670A0">
    </a>
</p>

## Overview
**Tokencal** is a sophisticated tool designed to estimate token prices for large language models (LLMs). Whether you're a developer, researcher, or business, Tokencal helps you optimize token usage and manage costs effectively. 

## Features
- **Accurate Estimations**: Get precise token price estimates for various LLMs.
- **Efficient Performance**: Fast calculations to save your time.
- **Extensive Compatibility**: Works with multiple LLMs.

## Installation
To install Tokencal, simply run:
```bash
pip install tokencal
```
## Usage
Using Tokencal is straightforward. Here’s a quick example to get you started:
```
from tokencal import TokenEstimator

# Initialize the estimator with your LLM model details
estimator = TokenEstimator(model_name="Your_LLM_Model")

# Estimate token price
price = estimator.estimate_price(text="Sample text to estimate token price.")
print(f"Estimated token price: {price}")
```

## Keywords
Token price estimation, LLM, Large Language Models, cost optimization, token usage
