@dqbd/tiktoken

⏳ tiktoken

tiktoken is a BPE tokeniser for use with OpenAI's models, forked from the original tiktoken library to provide NPM bindings for Node and other JS runtimes.

The open source version of tiktoken can be installed from NPM:

npm install @dqbd/tiktoken

Usage

Basic usage follows, which includes all the OpenAI encoders and ranks:

import assert from "node:assert";
import { get_encoding, encoding_for_model } from "@dqbd/tiktoken";

const enc = get_encoding("gpt2");
assert(
  new TextDecoder().decode(enc.decode(enc.encode("hello world"))) ===
    "hello world"
);

// To get the tokeniser corresponding to a specific model in the OpenAI API:
const enc = encoding_for_model("text-davinci-003");

// Extend existing encoding with custom special tokens
const enc = encoding_for_model("gpt2", {
  "<|im_start|>": 100264,
  "<|im_end|>": 100265,
});

// don't forget to free the encoder after it is not used
enc.free();

In constrained environments (eg. Edge Runtime, Cloudflare Workers), where you don't want to load all the encoders at once, you can use the lightweight WASM binary via @dqbd/tiktoken/lite.

const { Tiktoken } = require("@dqbd/tiktoken/lite");
const cl100k_base = require("@dqbd/tiktoken/encoders/cl100k_base.json");

const encoding = new Tiktoken(
  cl100k_base.bpe_ranks,
  cl100k_base.special_tokens,
  cl100k_base.pat_str
);
const tokens = encoding.encode("hello world");
encoding.free();

If you want to fetch the latest ranks, use the load function:

const { Tiktoken } = require("@dqbd/tiktoken/lite");
const { load } = require("@dqbd/tiktoken/load");
const registry = require("@dqbd/tiktoken/registry.json");
const models = require("@dqbd/tiktoken/model_to_encoding.json");

async function main() {
  const model = await load(registry[models["gpt-3.5-turbo"]]);
  const encoder = new Tiktoken(
    model.bpe_ranks,
    model.special_tokens,
    model.pat_str
  );
  const tokens = encoding.encode("hello world");
  encoder.free();
}

main();

If desired, you can create a Tiktoken instance directly with custom ranks, special tokens and regex pattern:

import { Tiktoken } from "../pkg";
import { readFileSync } from "fs";

const encoder = new Tiktoken(
  readFileSync("./ranks/gpt2.tiktoken").toString("utf-8"),
  { "<|endoftext|>": 50256, "<|im_start|>": 100264, "<|im_end|>": 100265 },
  "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+"
);

Finally, you can a custom init function to override the WASM initialization logic for non-Node environments. This is useful if you are using a bundler that does not support WASM ESM integration.

import { get_encoding, init } from "@dqbd/tiktoken/init";

async function main() {
  const wasm = "..."; // fetch the WASM binary somehow
  await init((imports) => WebAssembly.instantiate(wasm, imports));

  const encoding = get_encoding("cl100k_base");
  const tokens = encoding.encode("hello world");
  encoding.free();
}

main();

Compatibility

As this is a WASM library, there might be some issues with specific runtimes. If you encounter any issues, please open an issue.

Runtime	Status	Notes
Node.js	✅
Bun	✅
Vite	✅	See here for notes
Next.js	✅	See here for notes
Vercel Edge Runtime	🚧	See here for notes
Cloudflare Workers	🚧	See here for caveats
Deno	❌	Currently unsupported

Vite

If you are using Vite, you will need to add both the vite-plugin-wasm and vite-plugin-top-level-await. Add the following to your vite.config.js:

import wasm from "vite-plugin-wasm";
import topLevelAwait from "vite-plugin-top-level-await";
import { defineConfig } from "vite";

export default defineConfig({
  plugins: [wasm(), topLevelAwait()],
});

Next.js

Both API routes and /pages are supported with the following next.config.js configuration.

// next.config.json
const config = {
  webpack(config, { isServer, dev }) {
    config.experiments = {
      asyncWebAssembly: true,
      layers: true,
    };

    return config;
  },
};

Usage in pages:

import { get_encoding } from "@dqbd/tiktoken";
import { useState } from "react";

const encoding = get_encoding("cl100k_base");

export default function Home() {
  const [input, setInput] = useState("hello world");
  const tokens = encoding.encode(input);

  return (
    <div>
      <input
        type="text"
        value={input}
        onChange={(e) => setInput(e.target.value)}
      />
      <div>{tokens.toString()}</div>
    </div>
  );
}

Usage in API routes:

import { get_encoding } from "@dqbd/tiktoken";
import { NextApiRequest, NextApiResponse } from "next";

export default function handler(req: NextApiRequest, res: NextApiResponse) {
  const encoding = get_encoding("cl100k_base");
  const tokens = encoding.encode("hello world");
  encoding.free();
  return res.status(200).json({ tokens });
}

Vercel Edge Runtime

Vercel Edge Runtime does support WASM modules by adding a ?module suffix. Initialize the encoder with the following snippet:

// @ts-expect-error
import wasm from "@dqbd/tiktoken/lite/tiktoken_bg.wasm?module";
import model from "@dqbd/tiktoken/encoders/cl100k_base.json";
import { init, Tiktoken } from "@dqbd/tiktoken/lite/init";

export const config = { runtime: "edge" };

export default async function (req: Request) {
  await init((imports) => WebAssembly.instantiate(wasm, imports));

  const encoding = new Tiktoken(
    model.bpe_ranks,
    model.special_tokens,
    model.pat_str
  );

  const tokens = encoding.encode("hello world");
  encoding.free();

  return new Response(`${tokens}`);
}

Cloudflare Workers

Similar to Vercel Edge Runtime, Cloudflare Workers must import the WASM binary file manually and use the @dqbd/tiktoken/lite version to fit the 1 MB limit. However, users need to point directly at the WASM binary via a relative path (including ./node_modules/).

Add the following rule to the wrangler.toml to upload WASM during build:

[[rules]]
globs = ["**/*.wasm"]
type = "CompiledWasm"

Initialize the encoder with the following snippet:

import { init, Tiktoken } from "@dqbd/tiktoken/lite/init";
import wasm from "./node_modules/@dqbd/tiktoken/lite/tiktoken_bg.wasm";
import model from "@dqbd/tiktoken/encoders/cl100k_base.json";

export default {
  async fetch() {
    await init((imports) => WebAssembly.instantiate(wasm, imports));
    const encoder = new Tiktoken(
      model.bpe_ranks,
      model.special_tokens,
      model.pat_str
    );
    const tokens = encoder.encode("test");
    encoder.free();
    return new Response(`${tokens}`);
  },
};

Acknowledgements

• https://github.com/zurawiki/tiktoken-rs