In recent years, large language models (LLMs) such as GPT-3 have shown incredible promise in generating human-like text, answering questions, and holding conversations. However, to transform an LLM into a fully functional AI assistant, we need more than just text generation capabilities. That’s where LLMKit comes in. LLMKit is a powerful library designed to help developers convert their text-to-text LLMs into fully functional AI assistants, enabling them to perform specific tasks and handle real-world scenarios.
LLMKit simplifies the integration of LLMs into your applications by providing a modular plugin system, built-in retry mechanisms, and customizable conversation configurations. In this article, we will walk you through the process of creating a conversation with an external function call using LLMKit. By the end of this guide, you’ll have a solid understanding of how to extend the capabilities of your LLM and build a responsive AI assistant.
Getting Started with LLMKit
To start using LLMKit, you’ll need to install it from GitHub using npm. Open your terminal and run the following command:
A step-by-step guide to create a conversation
Step 1: Importing modules and creating instances
import { External, ExternalFunction } from “llmkit/plugins/external“;
// Import the OpenAI module here
also, instantiate the OpenAI client:
Step 2: Create the conversation
Don’t worry, we will explain the code below later:
(m) => retry({}, (num) => gpt.chatCompletion(/*or any function*/(m, {
debug: true
}))),
{
plugins: [
External.create([
ExternalFunction.create(“purchase“, {
description: “Orders something“,
parameters: {
name: “string, The name of the product“
},
hook: async ({ name }) => {
if (name.toLowerCase() == “pizza“) {
return “Ordered! Tell the user that the pizza is yummy“;
}
return “Product Not Found“;
},
}),
]),
],
}
);
Conversation is the main class in LLMKit that hold the conversation between three parts: system, user, agent
The Conversation.create() static function accepts two arguments:
The first argument, which is the function that’s called to pass the string message to the GPT. ( notice the retry function which repeats the function if it fails )
The second argument is the options object.
Here we added External to plugins, External provides a way for the GPT/LLM to execute functions your code.
Step 3: Send a Message to the Conversation
await conv.start();
let res = await conv.send(“I wanna purchase a burger“);
// You can access messages through conv.messages
console.log(res); // I’m sorry, I couldn’t find the burger you were looking for. How can I assist you further?
})();
After that all
If you still have any question, post it in the comments.
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