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A simple typescript langchain alternative for Open AI chat models.

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GPTIO

UPDATE: Many of the features this library provides have since been implemented nearly 1:1 into OpenAI's own API as Function Calling.

A simple typescript alternative to langchain custom tools in a few hundred LOC to buck all the pointless prompt psuedoscience.

Demo Gif


Start by installing:

npm i gptio

Set an environment variable:

OPENAI_API_KEY=sk-***************************

Everything you need to get started:

import GPTIO from "gptio";

// Inputs to "actions" are passed through a single input object
function add({a, b}) {
  return a + b;
}

function multiply({a, b}) {
  return a * b;
}

const gptio = new GPTIO(
  // OpenAI chat model (gpt-3.5-turbo or gpt-4)
  "gpt-4",
  // Actions to give GPTIO (your functions + descriptions)
  [
    {
      name: "add",
      description: "Adds two numbers together.",
      func: add,
      inputs: [
        {
          name: "a",
          type: "number", // Any valid JSON type
          description: "The first number to add.",
        },
        {
          name: "b",
          type: "number",
          description: "The second number to add.",
        },
      ],
    },
    {
      name: "multiply",
      description: "Multiplies two numbers together.",
      func: multiply,
      inputs: [
        {
          name: "a",
          type: "number",
          description: "The first number to multiply.",
        },
        {
          name: "b",
          type: "number",
          description: "The second number to multiply.",
        },
      ],
    }
  ],
  // Optional custom callbacks for progress reporting, custom memory schemes, or early exits
  {
    beforeAction: (action, input) => {
      // Add custom logic that should be run before each action
      // throw an error to interrupt and end the run
    },
    afterAction: (action, input, result) => {
      // ...run after each action is executed
    },
    afterThought: (thought) => {
      // ...run after each thought is generated
    }
  },
  // GPTIO options
  {
    // enable a built-in JS code execution action using safe-eval
    // in smooth brain terms: GPTIO can self-author custom tools
    evaluation: true,
    // print the entire chain of thought at the end
    debug: true,
    // timeout in seconds for the entire run
    timeout: 100
  }
);

gptio.run("Get me the current time and identify if the number of minutes is a prime number.").then((result) => {
  // results look like { result: any, message: string }
  // errors look like { error: true, message: string }
  console.log(result);
}).catch((error) => {
  // execution error (timeout, error thrown from callback, Open AI, etc.)
  console.log(error);
});

GPTIO will reason through and execute actions in a sequence to solve whatever task you give it in run(), and will attempt to inform you if the actions you've provided are insufficient.


Memory

Memory can be easily integrated with GPTIO in 2 easy steps /s

  1. Write a function remember (name it whatever you want) that accepts a query string as input, reads your memory provider (vector db, in memory store, idc), and returns a formatted string with the information you'd like to recall. Add it as an action into your GPTIO config.
  2. In the afterAction, save the result in your memory, better yet, tag it with action.name.

Or, feel free to deal with this.


If it’s missing a feature, do contribute to this repo, or just copy the source code– there isn’t a lot.

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