Count tokens

A versatile tool for counting tokens in text files, directories, and strings with support for streaming large files, batching, and more.

Table of Contents

• Count tokens
  • Table of Contents
  • Requirements
  • Installation
  • Usage
    • Basic Usage
    • Directory Processing
    • Large File Support
    • Output Formats
    • Token Limit Checking
  • Approximate number of tokens
  • Adjusting estimation rules
  • Programmatic usage
    • Simple API
    • Directory Processing
    • Streaming Large Files
    • Check Token Limits
    • Original API
  • Related Projects
  • Credits
  • License

Requirements

This package is using tiktoken library for tokenization.

Installation

For usage from command line install the package in isolated environment with pipx:

pipx install count-tokens

or run it with uv without installing:

uvx run count-tokens document.txt

or install it in your current environment with pip.

pip install count-tokens

Usage

Basic Usage

Open terminal and run:

count-tokens document.txt

You should see something like this:

File: document.txt
Encoding: cl100k_base
Number of tokens: 67

if you want to see just the tokens count run:

count-tokens document.txt --quiet

and the output will be:

67

To use count-tokens with other than default cl100k_base encoding use the additional input argument -e or --encoding:

count-tokens document.txt -e r50k_base

NOTE: tiktoken supports three encodings used by OpenAI models:

Encoding name	OpenAI models
o200k_base	gpt-4o, gpt-4o-mini
cl100k_base	gpt-4, gpt-3.5-turbo, text-embedding-ada-002
p50k_base	Codex models, text-davinci-002, text-davinci-003
r50k_base (or gpt2)	GPT-3 models like davinci

(source: OpenAI Cookbook)

Directory Processing

Process all files in a directory matching specific patterns:

count-tokens -d ./docs -p "*.md,*.txt"

Process directories recursively:

count-tokens -d ./project -r -p "*.py"

Large File Support

Use streaming mode for large files to avoid memory issues:

count-tokens large_file.txt --stream

Customize chunk size for streaming (default is 1MB):

count-tokens large_file.txt --stream --chunk-size 2097152

Output Formats

Get results in different formats:

# JSON format
count-tokens -d ./docs -p "*.md" --format json

# CSV format
count-tokens -d ./docs -p "*.md" --format csv

Token Limit Checking

Check if files exceed a specific token limit:

count-tokens document.txt --max-tokens 4096

When files exceed the limit, you'll see a warning:

File: document.txt
Encoding: cl100k_base
⚠️ Token limit exceeded: 5120 > 4096
Number of tokens: 5120

Approximate number of tokens

In case you need the results a bit faster and you don't need the exact number of tokens you can use the --approx parameter with w to have approximation based on number of words or c to have approximation based on number of characters.

count-tokens document.txt --approx w

It is based on assumption that there is 4/3 (1 and 1/3) tokens per word and 4 characters per token.

Adjusting estimation rules

You can customize the rules used for token estimation by adjusting the default values for tokens per word and characters per token ratios:

# Adjust the tokens per word ratio (default is 1.33)
count-tokens document.txt --approx w --tokens-per-word 1.5

# Adjust the characters per token ratio (default is 4.0)
count-tokens document.txt --approx c --characters-per-token 3.5

These options allow you to fine-tune the approximation based on your specific content characteristics.

Programmatic usage

Simple API

The package now provides a simplified API for all token counting operations:

from count_tokens import count

# Count tokens in a string
result = count(text="This is a string")

# Count tokens in a file
result = count(file="document.txt", encoding="cl100k_base")

# Count tokens with approximation
result = count(file="document.txt", approximate="w", tokens_per_word=1.5)

Directory Processing

Process all files in a directory that match specific patterns:

from count_tokens import count

# Process a directory
results = count(
    directory="./docs",
    file_patterns=["*.md", "*.txt"],
    recursive=True
)

# Print results
for file_path, token_count in results.items():
    print(f"{file_path}: {token_count} tokens")

Streaming Large Files

Process large files without loading the entire file into memory:

from count_tokens import count

# Process a large file with streaming
tokens = count(
    file="large_dataset.txt", 
    use_streaming=True,
    chunk_size=1024*1024  # 1MB chunks
)

Check Token Limits

Check if content exceeds token limits:

from count_tokens import count

# Check if a file exceeds token limit
result = count(file="document.txt", max_tokens=4096)

if isinstance(result, dict) and result.get("limit_exceeded"):
    print(f"⚠️ Token limit exceeded: {result['tokens']} > {result['max_tokens']}")

Original API

The original functions are still available for backward compatibility:

from count_tokens.count import count_tokens_in_file, count_tokens_in_string

# Count tokens in a file
num_tokens = count_tokens_in_file("document.txt")

# Count tokens in a string
num_tokens = count_tokens_in_string("This is a string.")

# Use specific encoding
num_tokens = count_tokens_in_string("This is a string.", encoding_name="cl100k_base")

# Word-based approximation with custom tokens per word ratio
num_tokens = count_tokens_in_file("document.txt", approximate="w", tokens_per_word=1.5)

# Character-based approximation with custom characters per token ratio
num_tokens = count_tokens_in_file("document.txt", approximate="c", characters_per_token=3.5)

Related Projects

• tiktoken - tokenization library used by this package
• ttok - count and truncate text based on tokens

Credits

Thanks to the authors of the tiktoken library for open sourcing their work.

License

MIT © Krystian Safjan.