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 tiktoken
Basic usage follows, which includes all the OpenAI encoders and ranks:
import assert from "node:assert";
import { get_encoding, encoding_for_model } from "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 tiktoken/lite.
const { Tiktoken } = require("tiktoken/lite");
const cl100k_base = require("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("tiktoken/lite");
const { load } = require("tiktoken/load");
const registry = require("tiktoken/registry.json");
const models = require("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 = encoder.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 "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();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 |
| Create React App (via Craco) | ✅ | See here for notes |
| Vercel Edge Runtime | ✅ | See here for notes |
| Cloudflare Workers | ✅ | See here for notes |
| Deno | ❌ | Currently unsupported |
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()],
});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 "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 "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 });
}By default, the Webpack configugration found in Create React App does not support WASM ESM modules. To add support, please do the following:
- Swap
react-scriptswithcraco, using the guide found here: https://craco.js.org/docs/getting-started/. - Add the following to
craco.config.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$/);
return config;
},
},
};Vercel Edge Runtime does support WASM modules by adding a ?module suffix. Initialize the encoder with the following snippet:
// @ts-expect-error
import wasm from "tiktoken/lite/tiktoken_bg.wasm?module";
import model from "tiktoken/encoders/cl100k_base.json";
import { init, Tiktoken } from "tiktoken/lite/init";
export const config = { runtime: "edge" };
export default async function (req: Request) {
await init((imports) => WebAssembly.instantiate(wasm, imports));
const encoding = new Tiktoken(
model.bpe_ranks,
model.special_tokens,
model.pat_str
);
const tokens = encoding.encode("hello world");
encoding.free();
return new Response(`${tokens}`);
}Similar to Vercel Edge Runtime, Cloudflare Workers must import the WASM binary file manually and use the 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 "tiktoken/lite/init";
import wasm from "./node_modules/tiktoken/lite/tiktoken_bg.wasm";
import model from "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}`);
},
};