tiktoken

README

tiktoken-go

OpenAI's tiktoken in Go, ported for use at Enterpret.

Tiktoken is a fast BPE tokeniser for use with OpenAI's models.

This is a port of the original tiktoken.

Usage

Install

go get github.com/aavaz-ai/tiktoken-go

Cache

Tiktoken-go has the same cache mechanism as the original Tiktoken library.

You can set the cache directory by using the environment variable TIKTOKEN_CACHE_DIR.

Once this variable is set, tiktoken-go will use this directory to cache the token dictionary.

If you don't set this environment variable, tiktoken-go will download the dictionary each time you initialize an encoding for the first time.

Example

get token by encoding

package main

import (
    "fmt"
    "github.com/aavaz-ai/tiktoken-go"
)

func main() (num_tokens int) {
    text = "Hello, world!"
    encoding = "r50k_base"

	tke, err := tiktoken.GetEncoding(encoding)
	if err != nil {
		err = fmt.Errorf("getEncoding: %v", err)
		return
	}

    // encode
	token := tke.Encode(text, nil, nil)

    // num_tokens
    num_tokens = len(token)
}

get token by Model

package main

import (
    "fmt"
    "github.com/aavaz-ai/tiktoken-go"
)

func main() (num_tokens int) {
    text = "Hello, world!"
    encoding = "davinci"

   tkm, err := tiktoken.EncodingForModel(model)
	if err != nil {
		err = fmt.Errorf("getEncoding: %v", err)
		return
	}

	 // encode
	token := tke.Encode(text, nil, nil)

    // num_tokens
    num_tokens = len(token)
}

counting tokens for chat API calls

Below is an example function for counting tokens for messages passed to gpt-3.5-turbo-0301 or gpt-4-0314.

The following code was written by @nasa1024 based on openai-cookbook examples.

Please note that the token calculation method for the message may change at any time, so this code may not necessarily be applicable in the future.

If you need accurate calculation, please refer to the official documentation.

If you find that this code is no longer applicable, please feel free to submit a PR or Issue.

package main

import (
	"fmt"

	"github.com/aavaz-ai/tiktoken-go"
	"github.com/sashabaranov/go-openai"
)

func NumTokensFromMessages(messages []openai.ChatCompletionMessage, model string) (num_tokens int) {
	tkm, err := tiktoken.EncodingForModel(model)
	if err != nil {
		err = fmt.Errorf("EncodingForModel: %v", err)
		fmt.Println(err)
		return
	}

	var tokens_per_message int
	var tokens_per_name int
	if model == "gpt-3.5-turbo-0301" || model == "gpt-3.5-turbo" {
		tokens_per_message = 4
		tokens_per_name = -1
	} else if model == "gpt-4-0314" || model == "gpt-4" {
		tokens_per_message = 3
		tokens_per_name = 1
	} else {
		fmt.Println("Warning: model not found. Using cl100k_base encoding.")
		tokens_per_message = 3
		tokens_per_name = 1
	}

	for _, message := range messages {
		num_tokens += tokens_per_message
		num_tokens += len(tkm.Encode(message.Content, nil, nil))
		num_tokens += len(tkm.Encode(message.Role, nil, nil))
		if message.Name != "" {
			num_tokens += tokens_per_name
		}
	}
	num_tokens += 3
	return num_tokens
}

available encodings

Encoding name	OpenAI models
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

available models

Model name	OpenAI models
gpt-4	cl100k_base
gpt-4-*	cl100k_base
gpt-3.5-turbo	cl100k_base
gpt-3.5-turbo-*	cl100k_base
text-davinci-003	p50k_base
text-davinci-002	p50k_base
text-davinci-001	r50k_base
text-curie-001	r50k_base
text-babbage-001	r50k_base
text-ada-001	r50k_base
davinci	r50k_base
curie	r50k_base
babbage	r50k_base
ada	r50k_base
code-davinci-002	p50k_base
code-davinci-001	p50k_base
code-cushman-002	p50k_base
code-cushman-001	p50k_base
davinci-codex	p50k_base
cushman-codex	p50k_base
text-davinci-edit-001	p50k_edit
code-davinci-edit-001	p50k_edit
text-embedding-ada-002	cl100k_base
text-similarity-davinci-001	r50k_base
text-similarity-curie-001	r50k_base
text-similarity-babbage-001	r50k_base
text-similarity-ada-001	r50k_base
text-search-davinci-doc-001	r50k_base
text-search-curie-doc-001	r50k_base
text-search-babbage-doc-001	r50k_base
text-search-ada-doc-001	r50k_base
code-search-babbage-code-001	r50k_base
code-search-ada-code-001	r50k_base
gpt2	gpt2

Documentation

Index

• Constants
• Variables
• type CoreBPE
  • func NewCoreBPE(encoder map[string]int, specialTokensEncoder map[string]int, pattern string) (*CoreBPE, error)
• type Encoding
• type Tiktoken
  • func EncodingForModel(modelName string) (*Tiktoken, error)
  • func GetEncoding(encodingName string) (*Tiktoken, error)
  • func (t *Tiktoken) Decode(tokens []int) string
  • func (t *Tiktoken) Encode(text string, allowedSpecial []string, disallowedSpecial []string) []int
  • func (t *Tiktoken) SpecialTokenRegex(disallowedSpecialSet map[string]any) *regexp2.Regexp

Constants

const ENDOFPROMPT string = "<|endofprompt|>"

const ENDOFTEXT string = "<|endoftext|>"

const FIM_MIDDLE string = "<|fim_middle|>"

const FIM_PREFIX string = "<|fim_prefix|>"

const FIM_SUFFIX string = "<|fim_suffix|>"

Variables

var ENCODING_MAP = map[string]*Encoding{}

var MODEL_PREFIX_TO_ENCODING = map[string]string{

	"gpt-4-":         "cl100k_base",
	"gpt-3.5-turbo-": "cl100k_base",
}

var MODEL_TO_ENCODING = map[string]string{

	"gpt-4":         "cl100k_base",
	"gpt-3.5-turbo": "cl100k_base",

	"text-davinci-003": "p50k_base",
	"text-davinci-002": "p50k_base",
	"text-davinci-001": "r50k_base",
	"text-curie-001":   "r50k_base",
	"text-babbage-001": "r50k_base",
	"text-ada-001":     "r50k_base",
	"davinci":          "r50k_base",
	"curie":            "r50k_base",
	"babbage":          "r50k_base",
	"ada":              "r50k_base",

	"code-davinci-002": "p50k_base",
	"code-davinci-001": "p50k_base",
	"code-cushman-002": "p50k_base",
	"code-cushman-001": "p50k_base",
	"davinci-codex":    "p50k_base",
	"cushman-codex":    "p50k_base",

	"text-davinci-edit-001": "p50k_edit",
	"code-davinci-edit-001": "p50k_edit",

	"text-embedding-ada-002": "cl100k_base",

	"text-similarity-davinci-001":  "r50k_base",
	"text-similarity-curie-001":    "r50k_base",
	"text-similarity-babbage-001":  "r50k_base",
	"text-similarity-ada-001":      "r50k_base",
	"text-search-davinci-doc-001":  "r50k_base",
	"text-search-curie-doc-001":    "r50k_base",
	"text-search-babbage-doc-001":  "r50k_base",
	"text-search-ada-doc-001":      "r50k_base",
	"code-search-babbage-code-001": "r50k_base",
	"code-search-ada-code-001":     "r50k_base",

	"gpt2": "gpt2",
}

Types

type CoreBPE

type CoreBPE struct {
	// contains filtered or unexported fields
}

func NewCoreBPE

func NewCoreBPE(encoder map[string]int, specialTokensEncoder map[string]int, pattern string) (*CoreBPE, error)

type Encoding

type Encoding struct {
	Name           string
	PatStr         string
	MergeableRanks map[string]int
	SpecialTokens  map[string]int
	ExplicitNVocab int
}

type Tiktoken

type Tiktoken struct {
	// contains filtered or unexported fields
}

func EncodingForModel

func EncodingForModel(modelName string) (*Tiktoken, error)

func GetEncoding

func GetEncoding(encodingName string) (*Tiktoken, error)

func (*Tiktoken) Decode

func (t *Tiktoken) Decode(tokens []int) string

func (*Tiktoken) Encode

func (t *Tiktoken) Encode(text string, allowedSpecial []string, disallowedSpecial []string) []int

func (*Tiktoken) SpecialTokenRegex

func (t *Tiktoken) SpecialTokenRegex(disallowedSpecialSet map[string]any) *regexp2.Regexp