# ⏳ tiktoken tiktoken is a [BPE](https://en.wikipedia.org/wiki/Byte_pair_encoding) 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: ```typescript 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`. ```typescript 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: ```typescript 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: ```typescript 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. ```typescript 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](#vite) for notes | | Next.js | ✅ | See [here](#nextjs) for notes | | Create React App (via Craco) | ✅ | See [here](#create-react-app) for notes | | Vercel Edge Runtime | ✅ | See [here](#vercel-edge-runtime) for notes | | Cloudflare Workers | ✅ | See [here](#cloudflare-workers) for notes | | Deno | ❌ | Currently unsupported | ### [Vite](#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`: ```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](#nextjs) Both API routes and `/pages` are supported with the following `next.config.js` configuration. ```typescript // next.config.json const config = { webpack(config, { isServer, dev }) { config.experiments = { asyncWebAssembly: true, layers: true, }; return config; }, }; ``` Usage in pages: ```tsx 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 (
setInput(e.target.value)} />
{tokens.toString()}
); } ``` Usage in API routes: ```typescript 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 }); } ``` ### [Create React App](#create-react-app) By default, the Webpack configugration found in Create React App does not support WASM ESM modules. To add support, please do the following: 1. Swap `react-scripts` with `craco`, using the guide found here: https://craco.js.org/docs/getting-started/. 2. Add the following to `craco.config.js`: ```js module.exports = { webpack: { configure: (config) => { config.experiments = { asyncWebAssembly: true, layers: true, }; // turn off static file serving of WASM files // we need to let Webpack handle WASM import config.module.rules .find((i) => "oneOf" in i) .oneOf.find((i) => i.type === "asset/resource") .exclude.push(/\.wasm$/); return config; }, }, }; ``` ### [Vercel Edge Runtime](#vercel-edge-runtime) Vercel Edge Runtime does support WASM modules by adding a `?module` suffix. Initialize the encoder with the following snippet: ```typescript // @ts-expect-error import wasm from "@dqbd/tiktoken/lite/tiktoken_bg.wasm?module"; import model from "@dqbd/tiktoken/encoders/cl100k_base.json"; import { init, Tiktoken } from "@dqbd/tiktoken/lite/init"; export const config = { runtime: "edge" }; export default async function (req: Request) { await init((imports) => WebAssembly.instantiate(wasm, imports)); const encoding = new Tiktoken( model.bpe_ranks, model.special_tokens, model.pat_str ); const tokens = encoding.encode("hello world"); encoding.free(); return new Response(`${tokens}`); } ``` ### [Cloudflare Workers](#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: ```toml [[rules]] globs = ["**/*.wasm"] type = "CompiledWasm" ``` Initialize the encoder with the following snippet: ```javascript 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