PyTokenCounter

PyTokenCounter is a Python library designed to simplify text tokenization and token counting. It supports various encoding schemes, with a focus on those used by Large Language Models (LLMs), particularly those developed by OpenAI. Leveraging the tiktoken library for efficient processing, PyTokenCounter facilitates seamless integration with LLM workflows. This project is based on the tiktoken library created by OpenAI.

Table of Contents

• Background
• Install
• Usage
  • CLI
• API
  • Utility Functions
  • String Tokenization and Counting
  • File and Directory Tokenization and Counting
  • Token Mapping
• Maintainers
• Acknowledgements
• Contributing
• License

Background

The development of PyTokenCounter was driven by the need for a user-friendly and efficient way to handle text tokenization in Python, particularly for applications that interact with Large Language Models (LLMs) like OpenAI's language models. LLMs process text by breaking it down into tokens, which are the fundamental units of input and output for these models. Tokenization, the process of converting text into a sequence of tokens, is a fundamental step in natural language processing and essential for optimizing interactions with LLMs.

Understanding and managing token counts is crucial when working with LLMs because it directly impacts aspects such as API usage costs, prompt length limitations, and response generation. PyTokenCounter addresses these needs by providing an intuitive interface for tokenizing strings, files, and directories, as well as counting the number of tokens based on different encoding schemes. With support for various OpenAI models and their associated encodings, PyTokenCounter is versatile enough to be used in a wide range of applications involving LLMs, such as prompt engineering, cost estimation, and monitoring usage.

Install

Install PyTokenCounter using pip:

pip install PyTokenCounter

Usage

Here are a few examples to get you started with PyTokenCounter, especially in the context of LLMs:

from pathlib import Path

import PyTokenCounter as tc
import tiktoken

# Count tokens in a string for an LLM model
numTokens = tc.GetNumTokenStr(
    string="This is a test string.", model="gpt-4o"
)
print(f"Number of tokens: {numTokens}")

# Count tokens in a file intended for LLM processing
filePath = Path("./TestFile.txt")
numTokensFile = tc.GetNumTokenFile(filePath=filePath, model="gpt-4o")
print(f"Number of tokens in file: {numTokensFile}")

# Count tokens in a directory of documents for batch processing with an LLM
dirPath = Path("./TestDir")
numTokensDir = tc.GetNumTokenDir(dirPath=dirPath, model="gpt-4o", recursive=True)
print(f"Number of tokens in directory: {numTokensDir}")

# Get the encoding for a specific LLM model
encoding = tc.GetEncoding(model="gpt-4o")

# Tokenize a string using a specific encoding for LLM input
tokens = tc.TokenizeStr(string="This is another test.", encoding=encoding)
print(f"Token IDs: {tokens}")

# Map tokens to their decoded strings
mappedTokens = tc.MapTokens(tokens=tokens, encoding=encoding)
print(f"Mapped tokens: {mappedTokens}")

# Count tokens in a string using the default model
numTokens = tc.GetNumTokenStr(string="This is a test string.")
print(f"Number of tokens: {numTokens}")

# Count tokens in a file using the default model
filePath = Path("./TestFile.txt")
numTokensFile = tc.GetNumTokenFile(filePath=filePath)
print(f"Number of tokens in file: {numTokensFile}")

# Tokenize a string using the default model
tokens = tc.TokenizeStr(string="This is another test.")
print(f"Token IDs: {tokens}")

# Map tokens to their decoded strings using the default model
mappedTokens = tc.MapTokens(tokens=tokens)
print(f"Mapped tokens: {mappedTokens}")

CLI

PyTokenCounter can also be used as a command-line tool, making it convenient to integrate into scripts and workflows that involve LLMs:

# Example usage for tokenizing a string for an LLM
tokencount tokenize-str "Hello, world!" --model gpt-4o

# Example usage for tokenizing a string using the default model
tokencount tokenize-str "Hello, world!"

# Example usage for tokenizing a file for an LLM
tokencount tokenize-file TestFile.txt --model gpt-4o

# Example usage for tokenizing a file using the default model
tokencount tokenize-file TestFile.txt

# Example usage for tokenizing multiple files for an LLM
tokencount tokenize-files TestFile1.txt TestFile2.txt --model gpt-4o

# Example usage for tokenizing multiple files using the default model
tokencount tokenize-files TestFile1.txt TestFile2.txt

# Example usage for tokenizing a directory of files for an LLM
tokencount tokenize-files MyDirectory --model gpt-4o --no-recursive

# Example usage for tokenizing a directory of files using the default model
tokencount tokenize-files MyDirectory --no-recursive

# Example usage for tokenizing a directory of files for an LLM (alternative)
tokencount tokenize-dir MyDirectory --model gpt-4o --no-recursive

# Example usage for tokenizing a directory of files using the default model (alternative)
tokencount tokenize-dir MyDirectory --no-recursive

# Example usage for counting tokens in a string for an LLM
tokencount count-str "This is a test string." --model gpt-4o

# Example usage for counting tokens in a string using the default model
tokencount count-str "This is a test string."

# Example usage for counting tokens in a file for an LLM
tokencount count-file TestFile.txt --model gpt-4o

# Example usage for counting tokens in a file using the default model
tokencount count-file TestFile.txt

# Example usage for counting tokens in multiple files for an LLM
tokencount count-files TestFile1.txt TestFile2.txt --model gpt-4o

# Example usage for counting tokens in multiple files using the default model
tokencount count-files TestFile1.txt TestFile2.txt

# Example usage for counting tokens in a directory for an LLM
tokencount count-files TestDir --model gpt-4o --no-recursive

# Example usage for counting tokens in a directory using the default model
tokencount count-files TestDir --no-recursive

# Example usage for counting tokens in a directory for an LLM (alternative)
tokencount count-dir TestDir --model gpt-4o --no-recursive

# Example usage for counting tokens in a directory using the default model (alternative)
tokencount count-dir TestDir --no-recursive

# Example to get the model associated with an encoding
tokencount get-model cl100k_base

# Example to get the encoding associated with a model
tokencount get-encoding gpt-4o

# Example to map tokens to strings for an LLM
tokencount map-tokens 123,456,789 --model gpt-4o

# Example to map tokens to strings using the default model
tokencount map-tokens 123,456,789

CLI Usage Details:

The tokencount CLI provides several subcommands for tokenizing and counting tokens in strings, files, and directories, tailored for use with LLMs.

Subcommands:

• tokenize-str: Tokenizes a provided string.
  • tokencount tokenize-str "Your string here" --model gpt-4o
  • tokencount tokenize-str "Your string here"
• tokenize-file: Tokenizes the contents of a file.
  • tokencount tokenize-file Path/To/Your/File.txt --model gpt-4o
  • tokencount tokenize-file Path/To/Your/File.txt
• tokenize-files: Tokenizes the contents of multiple specified files or all files within a directory.
  • tokencount tokenize-files Path/To/Your/File1.txt Path/To/Your/File2.txt --model gpt-4o
  • tokencount tokenize-files Path/To/Your/File1.txt Path/To/Your/File2.txt
  • tokencount tokenize-files Path/To/Your/Directory --model gpt-4o --no-recursive
  • tokencount tokenize-files Path/To/Your/Directory --no-recursive
• tokenize-dir: Tokenizes all files within a specified directory into lists of token IDs.
  • tokencount tokenize-dir Path/To/Your/Directory --model gpt-4o --no-recursive
  • tokencount tokenize-dir Path/To/Your/Directory --no-recursive
• count-str: Counts the number of tokens in a provided string.
  • tokencount count-str "Your string here" --model gpt-4o
  • tokencount count-str "Your string here"
• count-file: Counts the number of tokens in a file.
  • tokencount count-file Path/To/Your/File.txt --model gpt-4o
  • tokencount count-file Path/To/Your/File.txt
• count-files: Counts the number of tokens in multiple specified files or all files within a directory.
  • tokencount count-files Path/To/Your/File1.txt Path/To/Your/File2.txt --model gpt-4o
  • tokencount count-files Path/To/Your/File1.txt Path/To/Your/File2.txt
  • tokencount count-files Path/To/Your/Directory --model gpt-4o --no-recursive
  • tokencount count-files Path/To/Your/Directory --no-recursive
• count-dir: Counts the total number of tokens across all files in a specified directory.
  • tokencount count-dir Path/To/Your/Directory --model gpt-4o --no-recursive
  • tokencount count-dir Path/To/Your/Directory --no-recursive
• get-model: Retrieves the model name from the provided encoding.
  • tokencount get-model cl100k_base
• get-encoding: Retrieves the encoding name from the provided model.
  • tokencount get-encoding gpt-4o
• map-tokens: Maps a list of token integers to their decoded strings.
  • tokencount map-tokens 123,456,789 --model gpt-4o
  • tokencount map-tokens 123,456,789

Options:

• -m, --model: Specifies the model to use for encoding. Default: gpt-4o
• -e, --encoding: Specifies the encoding to use directly.
• -nr, --no-recursive: When used with tokenize-files, tokenize-dir, count-files, or count-dir for a directory, it prevents the tool from processing subdirectories recursively.
• -q, --quiet: When used with any of the above commands, it prevents the tool from showing progress bars and minimizes output.
• -M, --mapTokens: When used with the tokenize-str, tokenize-file, tokenize-files, or tokenize-dir commands, outputs mapped tokens instead of raw token integers.
• -o, --output: When used with any of the commands, specifies an output JSON file to save the results to.

Note: For detailed help on each subcommand, use tokencount <subcommand> -h.

API

Here's a detailed look at the PyTokenCounter API, designed to integrate seamlessly with LLM workflows:

Utility Functions

GetModelMappings() -> dict

Retrieves the mappings between models and their corresponding encodings, essential for selecting the correct tokenization strategy for different LLMs.

Returns:

• dict: A dictionary where keys are model names and values are their corresponding encodings.

Example:

import PyTokenCounter as tc

modelMappings = tc.GetModelMappings()
print(modelMappings)

---

GetValidModels() -> list[str]

Returns a list of valid model names supported by PyTokenCounter, primarily focusing on LLMs.

Returns:

• list[str]: A list of valid model names.

Example:

import PyTokenCounter as tc

validModels = tc.GetValidModels()
print(validModels)

---

GetValidEncodings() -> list[str]

Returns a list of valid encoding names, ensuring compatibility with various LLMs.

Returns:

• list[str]: A list of valid encoding names.

Example:

import PyTokenCounter as tc

validEncodings = tc.GetValidEncodings()
print(validEncodings)

---

GetModelForEncoding(encoding: tiktoken.Encoding) -> list[str] | str

Determines the model name(s) associated with a given encoding, facilitating the selection of appropriate LLMs.

Parameters:

• encoding (tiktoken.Encoding): The encoding to get the model for.

Returns:

• str: The model name or a list of models corresponding to the given encoding.

Raises:

• ValueError: If the encoding name is not valid.

Example:

import PyTokenCounter as tc
import tiktoken

encoding = tiktoken.get_encoding('cl100k_base')
model = tc.GetModelForEncoding(encoding=encoding)
print(model)

---

GetModelForEncodingName(encodingName: str) -> str

Determines the model name associated with a given encoding name, facilitating the selection of appropriate LLMs.

Parameters:

• encodingName (str): The name of the encoding.

Returns:

• str: The model name or a list of models corresponding to the given encoding.

Raises:

• ValueError: If the encoding name is not valid.

Example:

import PyTokenCounter as tc

modelName = tc.GetModelForEncodingName(encodingName="cl100k_base")
print(modelName)

---

GetEncodingForModel(modelName: str) -> tiktoken.Encoding

Retrieves the encoding associated with a given model name, ensuring accurate tokenization for the selected LLM.

Parameters:

• modelName (str): The name of the model.

Returns:

• tiktoken.Encoding: The encoding corresponding to the given model name.

Raises:

• ValueError: If the model name is not valid.

Example:

import PyTokenCounter as tc

encoding = tc.GetEncodingForModel(modelName="gpt-4o")
print(encoding)

---

GetEncodingNameForModel(modelName: str) -> str

Retrieves the encoding name associated with a given model name, ensuring accurate tokenization for the selected LLM.

Parameters:

• modelName (str): The name of the model.

Returns:

• str: The encoding name corresponding to the given model name.

Raises:

• ValueError: If the model name is not valid.

Example:

import PyTokenCounter as tc

encodingName = tc.GetEncodingNameForModel(modelName="gpt-4o")
print(encodingName)

---

GetEncoding(model: str | None = None, encodingName: str | None = None) -> tiktoken.Encoding

Obtains the tiktoken encoding based on the specified model or encoding name, tailored for LLM usage. If neither model nor encodingName is provided, it defaults to the encoding associated with the "gpt-4o" model.

Parameters:

• model (str, optional): The name of the model.
• encodingName (str, optional): The name of the encoding.

Returns:

• tiktoken.Encoding: The tiktoken encoding object.

Raises:

• ValueError: If neither model nor encoding is provided, or if the provided model or encoding is invalid.

Example:

import PyTokenCounter as tc
import tiktoken

encoding = tc.GetEncoding(model="gpt-4o")
print(encoding)
encoding = tc.GetEncoding(encodingName="p50k_base")
print(encoding)
encoding = tc.GetEncoding()
print(encoding)

---

String Tokenization and Counting

TokenizeStr(string: str, model: str | None = "gpt-4o", encodingName: str | None = None, encoding: tiktoken.Encoding | None = None, quiet: bool = False, mapTokens: bool = True) -> list[int] | dict[str, int]

Tokenizes a string into a list of token IDs or a mapping of decoded strings to tokens, preparing text for input into an LLM.

Parameters:

• string (str): The string to tokenize.
• model (str, optional): The name of the model. Default: "gpt-4o"
• encodingName (str, optional): The name of the encoding.
• encoding (tiktoken.Encoding, optional): A tiktoken encoding object.
• quiet (bool, optional): If True, suppresses progress updates.
• mapTokens (bool, optional): If True, outputs a dictionary mapping decoded strings to their token IDs.

Returns:

• list[int]: A list of token IDs.
• dict[str, int]: A dictionary mapping decoded strings to token IDs if mapTokens is True.

Raises:

• ValueError: If the provided model or encoding is invalid.

Example:

import PyTokenCounter as tc

tokens = tc.TokenizeStr(string="Hail to the Victors!", model="gpt-4o")
print(tokens)

tokens = tc.TokenizeStr(string="Hail to the Victors!")
print(tokens)


import tiktoken
encoding = tiktoken.get_encoding("cl100k_base")
tokens = tc.TokenizeStr(string="2024 National Champions", encoding=encoding, mapTokens=True)
print(tokens)

tokens = tc.TokenizeStr(string="2024 National Champions", mapTokens=True)
print(tokens)

---

GetNumTokenStr(string: str, model: str | None = "gpt-4o", encodingName: str | None = None, encoding: tiktoken.Encoding | None = None, quiet: bool = False) -> int

Counts the number of tokens in a string.

Parameters:

• string (str): The string to count tokens in.
• model (str, optional): The name of the model. Default: "gpt-4o"
• encodingName (str, optional): The name of the encoding.
• encoding (tiktoken.Encoding, optional): A tiktoken.Encoding object.
• quiet (bool, optional): If True, suppresses progress updates.

Returns:

• int: The number of tokens in the string.

Raises:

• ValueError: If the provided model or encoding is invalid.

Example:

import PyTokenCounter as tc

numTokens = tc.GetNumTokenStr(string="Hail to the Victors!", model="gpt-4o")
print(numTokens)

numTokens = tc.GetNumTokenStr(string="Hail to the Victors!")
print(numTokens)

numTokens = tc.GetNumTokenStr(string="Corum 4 Heisman", encoding=tiktoken.get_encoding("cl100k_base"))
print(numTokens)

numTokens = tc.GetNumTokenStr(string="Corum 4 Heisman")
print(numTokens)

---

File and Directory Tokenization and Counting

TokenizeFile(filePath: Path | str, model: str | None = "gpt-4o", encodingName: str | None = None, encoding: tiktoken.Encoding | None = None, quiet: bool = False, mapTokens: bool = True) -> list[int] | dict[str, int]

Tokenizes the contents of a file into a list of token IDs or a mapping of decoded strings to tokens.

Parameters:

• filePath (Path | str): The path to the file to tokenize.
• model (str, optional): The name of the model to use for encoding. Default: "gpt-4o"
• encodingName (str, optional): The name of the encoding to use.
• encoding (tiktoken.Encoding, optional): An existing tiktoken.Encoding object to use for tokenization.
• quiet (bool, optional): If True, suppresses progress updates.
• mapTokens (bool, optional): If True, outputs a dictionary mapping decoded strings to their token IDs.

Returns:

• list[int]: A list of token IDs representing the tokenized file contents.
• dict[str, int]: A dictionary mapping decoded strings to token IDs if mapTokens is True.

Raises:

• TypeError: If the types of input parameters are incorrect.
• ValueError: If the provided model or encoding is invalid.
• UnsupportedEncodingError: If the file encoding is not supported.
• FileNotFoundError: If the file does not exist.

Example:

from pathlib import Path
import PyTokenCounter as tc

filePath = Path("TestFile1.txt")
tokens = tc.TokenizeFile(filePath=filePath, model="gpt-4o")
print(tokens)

filePath = Path("TestFile1.txt")
tokens = tc.TokenizeFile(filePath=filePath)
print(tokens)

import tiktoken
encoding = tiktoken.get_encoding("p50k_base")
filePath = Path("TestFile2.txt")
tokens = tc.TokenizeFile(filePath=filePath, encoding=encoding, mapTokens=True)
print(tokens)

filePath = Path("TestFile2.txt")
tokens = tc.TokenizeFile(filePath=filePath, mapTokens=True)
print(tokens)

---

GetNumTokenFile(filePath: Path | str, model: str | None = "gpt-4o", encodingName: str | None = None, encoding: tiktoken.Encoding | None = None, quiet: bool = False) -> int

Counts the number of tokens in a file based on the specified model or encoding.

Parameters:

• filePath (Path | str): The path to the file to count tokens for.
• model (str, optional): The name of the model to use for encoding. Default: "gpt-4o"
• encodingName (str, optional): The name of the encoding to use.
• encoding (tiktoken.Encoding, optional): An existing tiktoken.Encoding object to use for tokenization.
• quiet (bool, optional): If True, suppresses progress updates.

Returns:

• int: The number of tokens in the file.

Raises:

• TypeError: If the types of filePath, model, encodingName, or encoding are incorrect.
• ValueError: If the provided model or encodingName is invalid, or if there is a mismatch between the model and encoding name, or between the provided encoding and the derived encoding.
• UnsupportedEncodingError: If the file's encoding cannot be determined.
• FileNotFoundError: If the file does not exist.

Example:

import PyTokenCounter as tc
from pathlib import Path

filePath = Path("TestFile1.txt")
numTokens = tc.GetNumTokenFile(filePath=filePath, model="gpt-4o")
print(numTokens)

filePath = Path("TestFile1.txt")
numTokens = tc.GetNumTokenFile(filePath=filePath)
print(numTokens)

filePath = Path("TestFile2.txt")
numTokens = tc.GetNumTokenFile(filePath=filePath, model="gpt-4o")
print(numTokens)

filePath = Path("TestFile2.txt")
numTokens = tc.GetNumTokenFile(filePath=filePath)
print(numTokens)

---

TokenizeFiles(inputPath: Path | str | list[Path | str], model: str | None = "gpt-4o", encodingName: str | None = None, encoding: tiktoken.Encoding | None = None, recursive: bool = True, quiet: bool = False, exitOnListError: bool = True, mapTokens: bool = True) -> list[int] | dict[str, list[int] | dict]

Tokenizes multiple files or all files within a directory into lists of token IDs or a mapping of decoded strings to tokens.

Parameters:

• inputPath (Path | str | list[Path | str]): The path to a file or directory, or a list of file paths to tokenize.
• model (str, optional): The name of the model to use for encoding. Default: "gpt-4o"
• encodingName (str, optional): The name of the encoding to use.
• encoding (tiktoken.Encoding, optional): An existing tiktoken.Encoding object to use for tokenization.
• recursive (bool, optional): If inputPath is a directory, whether to tokenize files in subdirectories recursively. Default: True
• quiet (bool, optional): If True, suppresses progress updates. Default: False
• exitOnListError (bool, optional): If True, stop processing the list upon encountering an error. If False, skip files that cause errors. Default: True
• mapTokens (bool, optional): If True, outputs a dictionary mapping decoded strings to their token IDs for each file.

Returns:

• list[int] | dict[str, list[int] | dict]:
  • If inputPath is a file, returns a list of token IDs for that file.
  • If inputPath is a list of files, returns a dictionary where each key is the file name and the value is the list of token IDs for that file.
  • If inputPath is a directory:
  • If recursive is True, returns a nested dictionary where each key is a file or subdirectory name with corresponding token lists or sub-dictionaries.
  • If recursive is False, returns a dictionary with file names as keys and their token lists as values.

Raises:

• TypeError: If the types of inputPath, model, encodingName, encoding, or recursive are incorrect.
• ValueError: If any of the provided file paths in a list are not files, or if a provided directory path is not a directory.
• UnsupportedEncodingError: If any of the files to be tokenized have an unsupported encoding.
• RuntimeError: If the provided inputPath is neither a file, a directory, nor a list.

Example:

from PyTokenCounter import TokenizeFiles
from pathlib import Path

inputFiles = [
    Path("TestFile1.txt"),
    Path("TestFile2.txt"),
]
tokens = tc.TokenizeFiles(inputPath=inputFiles, model="gpt-4o")
print(tokens)

tokens = tc.TokenizeFiles(inputPath=inputFiles)
print(tokens)

# Tokenizing multiple files using the default model
tokens = tc.TokenizeFiles(inputPath=inputFiles)
print(tokens)

import tiktoken
encoding = tiktoken.get_encoding('p50k_base')
dirPath = Path("TestDir")
tokens = tc.TokenizeFiles(inputPath=dirPath, encoding=encoding, recursive=False)
print(tokens)

tokens = tc.TokenizeFiles(inputPath=dirPath, model="gpt-4o", recursive=True, mapTokens=True)
print(tokens)

tokens = tc.TokenizeFiles(inputPath=dirPath, recursive=True, mapTokens=True)
print(tokens)


# Tokenizing a directory using the default model
tokens = tc.TokenizeFiles(inputPath=dirPath, recursive=True, mapTokens=True)
print(tokens)

---

GetNumTokenFiles(inputPath: Path | str | list[Path | str], model: str | None = "gpt-4o", encodingName: str | None = None, encoding: tiktoken.Encoding | None = None, recursive: bool = True, quiet: bool = False, exitOnListError: bool = True) -> int

Counts the number of tokens across multiple files or in all files within a directory.

Parameters:

• inputPath (Path | str | list[Path | str]): The path to a file or directory, or a list of file paths to count tokens for.
• model (str, optional): The name of the model to use for encoding. Default: "gpt-4o"
• encodingName (str, optional): The name of the encoding to use.
• encoding (tiktoken.Encoding, optional): An existing tiktoken.Encoding object to use for tokenization.
• recursive (bool, optional): If inputPath is a directory, whether to count tokens in files in subdirectories recursively. Default: True
• quiet (bool, optional): If True, suppresses progress updates. Default: False
• exitOnListError (bool, optional): If True, stop processing the list upon encountering an error. If False, skip files that cause errors. Default: True

Returns:

• int: The total number of tokens in the specified files or directory.

Raises:

• TypeError: If the types of inputPath, model, encodingName, encoding, or recursive are incorrect.
• ValueError: If any of the provided file paths in a list are not files, or if a provided directory path is not a directory, or if the provided model or encoding is invalid.
• UnsupportedEncodingError: If any of the files to be tokenized have an unsupported encoding.
• RuntimeError: If the provided inputPath is neither a file, a directory, nor a list.

Example:

import PyTokenCounter as tc
from pathlib import Path

inputFiles = [
    Path("TestFile1.txt"),
    Path("TestFile2.txt"),
]
numTokens = tc.GetNumTokenFiles(inputPath=inputFiles, model='gpt-4o')
print(numTokens)


numTokens = tc.GetNumTokenFiles(inputPath=inputFiles)
print(numTokens)

# Counting tokens in multiple files using the default model
numTokens = tc.GetNumTokenFiles(inputPath=inputFiles)
print(numTokens)


import tiktoken
encoding = tiktoken.get_encoding('p50k_base')
dirPath = Path("TestDir")
numTokens = tc.GetNumTokenFiles(inputPath=dirPath, encoding=encoding, recursive=False)
print(numTokens)
numTokens = tc.GetNumTokenFiles(inputPath=dirPath, model='gpt-4o', recursive=True)
print(numTokens)

# Counting tokens in a directory using the default model
numTokens = tc.GetNumTokenFiles(inputPath=dirPath, recursive=True)
print(numTokens)

---

TokenizeDir(dirPath: Path | str, model: str | None = "gpt-4o", encodingName: str | None = None, encoding: tiktoken.Encoding | None = None, recursive: bool = True, quiet: bool = False, mapTokens: bool = True) -> dict[str, list[int] | dict]

Tokenizes all files within a directory into lists of token IDs or a mapping of decoded strings to tokens.

Parameters:

• dirPath (Path | str): The path to the directory to tokenize.
• model (str, optional): The name of the model to use for encoding. Default: "gpt-4o"
• encodingName (str, optional): The name of the encoding to use.
• encoding (tiktoken.Encoding, optional): An existing tiktoken.Encoding object to use for tokenization.
• recursive (bool, optional): Whether to tokenize files in subdirectories recursively. Default: True
• quiet (bool, optional): If True, suppresses progress updates. Default: False
• mapTokens (bool, optional): If True, outputs a dictionary mapping decoded strings to their token IDs for each file.

Returns:

• dict[str, list[int] | dict]: A nested dictionary where each key is a file or subdirectory name:
  • If the key is a file, its value is a list of token IDs.
  • If the key is a subdirectory, its value is another dictionary following the same structure.

Raises:

• TypeError: If the types of input parameters are incorrect.
• ValueError: If the provided path is not a directory or if the model or encoding is invalid.
• UnsupportedEncodingError: If the file's encoding cannot be determined.
• FileNotFoundError: If the directory does not exist.

Example:

import PyTokenCounter as tc
from pathlib import Path

dirPath = Path("TestDir")
tokenizedDir = tc.TokenizeDir(dirPath=dirPath, model="gpt-4o", recursive=True)
print(tokenizedDir)

tokenizedDir = tc.TokenizeDir(dirPath=dirPath, recursive=True)
print(tokenizedDir)

# Tokenizing a directory using the default model
tokenizedDir = tc.TokenizeDir(dirPath=dirPath, recursive=True)
print(tokenizedDir)

tokenizedDir = tc.TokenizeDir(dirPath=dirPath, model="gpt-4o", recursive=False)
print(tokenizedDir)

tokenizedDir = tc.TokenizeDir(dirPath=dirPath, recursive=False)
print(tokenizedDir)


tokenizedDir = tc.TokenizeDir(dirPath=dirPath, model="gpt-4o", recursive=True, mapTokens=True)
print(tokenizedDir)

# Tokenizing a directory using the default model with token mapping
tokenizedDir = tc.TokenizeDir(dirPath=dirPath, recursive=True, mapTokens=True)
print(tokenizedDir)

---

GetNumTokenDir(dirPath: Path | str, model: str | None = "gpt-4o", encodingName: str | None = None, encoding: tiktoken.Encoding | None = None, recursive: bool = True, quiet: bool = False) -> int

Counts the number of tokens in all files within a directory.

Parameters:

• dirPath (Path | str): The path to the directory to count tokens for.
• model (str, optional): The name of the model to use for encoding. Default: "gpt-4o"
• encodingName (str, optional): The name of the encoding to use.
• encoding (tiktoken.Encoding, optional): An existing tiktoken.Encoding object to use for tokenization.
• recursive (bool, optional): Whether to count tokens in subdirectories recursively. Default: True
• quiet (bool, optional): If True, suppresses progress updates. Default: False

Returns:

• int: The total number of tokens in the directory.

Raises:

• TypeError: If the types of input parameters are incorrect.
• ValueError: If the provided path is not a directory or if the model or encoding is invalid.
• UnsupportedEncodingError: If the file's encoding cannot be determined.
• FileNotFoundError: If the directory does not exist.

Example:

import PyTokenCounter as tc
from pathlib import Path

dirPath = Path("TestDir")
numTokensDir = tc.GetNumTokenDir(dirPath=dirPath, model="gpt-4o", recursive=True)
print(numTokensDir)

# Counting tokens in a directory using the default model
numTokensDir = tc.GetNumTokenDir(dirPath=dirPath, recursive=True)
print(numTokensDir)

numTokensDir = tc.GetNumTokenDir(dirPath=dirPath, model="gpt-4o", recursive=False)
print(numTokensDir)

numTokensDir = tc.GetNumTokenDir(dirPath=dirPath, recursive=False)
print(numTokensDir)

---

Token Mapping

MapTokens(tokens: list[int] | OrderedDict[str, list[int] | OrderedDict], model: str | None = "gpt-4o", encodingName: str | None = None, encoding: tiktoken.Encoding | None = None) -> OrderedDict[str, int] | OrderedDict[str, OrderedDict[str, int] | OrderedDict]

Maps tokens to their corresponding decoded strings based on a specified encoding.

Parameters:

• tokens (list[int] | OrderedDict[str, list[int] | OrderedDict]): The tokens to be mapped. This can either be:
  • A list of integer tokens to decode.
  • An OrderedDict with string keys and values that are either:
    • A list of integer tokens.
    • Another nested OrderedDict with the same structure.
• model (str, optional): The model name to use for determining the encoding. Default: "gpt-4o"
• encodingName (str, optional): The name of the encoding to use.
• encoding (tiktoken.Encoding, optional): The encoding object to use.

Returns:

• OrderedDict[str, int] | OrderedDict[str, OrderedDict[str, int] | OrderedDict]: A mapping of decoded strings to their corresponding integer tokens. If tokens is a nested structure, the result will maintain the same nested structure with decoded mappings.

Raises:

• TypeError: If tokens is not a list of integers or an OrderedDict of strings mapped to tokens.
• ValueError: If an invalid model or encoding name is provided, or if the encoding does not match the model or encoding name.
• KeyError: If a token is not in the given encoding's vocabulary.
• RuntimeError: If an unexpected error occurs while validating the encoding.

Example:

import PyTokenCounter as tc
import tiktoken
from collections import OrderedDict

encoding = tiktoken.get_encoding("cl100k_base")
tokens = [123,456,789]
mapped = tc.MapTokens(tokens=tokens, encoding=encoding)
print(mapped)

tokens = OrderedDict({
    "file1": [123,456,789],
    "file2": [987,654,321],
    "subdir": OrderedDict({
        "file3": [246, 135, 798],
        "file4": [951, 753, 864]
    })
})

mapped = tc.MapTokens(tokens=tokens, encoding=encoding)
print(mapped)

# Mapping tokens using the default model
mapped = tc.MapTokens(tokens=tokens)
print(mapped)

---

Maintainers

• Kaden Gruizenga

Acknowledgements

• This project is based on the tiktoken library created by OpenAI.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.