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Tutorial Part 1 - The basics

In this tutorial, we're going to introduce the basic concepts of AI.JSX one step at a time. All of the code for these tutorials can be found on GitHub at https://github.com/fixie-ai/ai-jsx/tree/main/packages/tutorial.

Let's start with the basic "hello world" example for AI.JSX, which invokes a Large Language Model with a fixed prompt, and prints the result to the console. Here is the complete application:

import * as AI from 'ai-jsx';
import { ChatCompletion, UserMessage } from 'ai-jsx/core/completion';

const app = (
<UserMessage>Write a Shakespearean sonnet about AI models.</UserMessage>

const renderContext = AI.createRenderContext();
const response = await renderContext.render(app);

You can run this yourself from the AI.JSX tree by running:

yarn workspace tutorial run part1

How it works

The first part of the code defines the variable app, which consists of a JSX component tree that will be rendered by the AI.JSX runtime. This is very similar to React, which uses JSX components to generate a DOM tree. Here, we are using JSX to describe the structure of the LLM model invocations and prompts in a compact, declarative way.

The <ChatCompletion> component is the root of the tree, and is responsible for generating a single invocation of the LLM -- in this case, OpenAI's ChatGPT model -- and returning the result. The <UserMessage> component simply tags the prompt as being the "user" component of the prompt -- as opposed to <SystemMessage>, which would be the "system" component of the prompt. These distinctions are used by OpenAI's API to differentiate between the fixed, system aspect of the prompt and the variable, user aspect of the prompt.

In order to actually get a result, we need to pass our application to a RenderContext and call render() on it. The RenderContext is responsible for managing the state of the application and progressively evaluating the state of the application as it is rendered. The render() method returns a Promise that evaluates to a string, which is the final result of rendering the JSX object tree.

We get a RenderContext by calling AI.createRenderContext(), and then call render() on it with our app object. The result is a string that we print to the console.