openai-node VS askai

Okay, now we've confirmed the API endpoint is working, let's connect it to OpenAI first, install the OpenAI package, navigate to the route directory, and run the command below in our terminal

For my setup, I am using the node version of the openai sdk.

We try to keep it to a minimum, especially in JS (though we have some nice improvements coming soon when we deprecate node-fetch in favor of built-in fetch). The package sizes aren't tiny because we include thorough types and sourcemaps, but the bundle sizes are fairly tidy.

Here's an example of a typical RESTful endpoint (Lithic's `client.cards.create()`:

https://github.com/lithic-com/lithic-node/blob/36d4a6a70597e...

Here are some example repos produced by Stainless:

1. https://github.com/openai/openai-node

Have you seen/tried the `.runTools()` helper?

Docs: https://github.com/openai/openai-node?tab=readme-ov-file#aut...

Example: https://github.com/openai/openai-node/blob/bb4bce30ff1bfb06d...

(if what you're fundamentally trying to do is really just get JSON out, then I can see how json_mode is still easier).

And so, I impulsively upgraded to the latest version of openai (I guess not anymore) without the fear of getting cut by cutting edge 😝 and got it working for some random text

UPDATE 2023/11/15: I used fetch and custom streams because at the time of writing, the openai module on NPM did not properly support streaming responses. That issue has been fixed, and I think a better solution would be to use that module and pipe their data through a TransformStream to send to the client. That version is not reflected here.

You may notice the JavaScript package available on NPM called openai. We will not be using this, as it doesn’t quite support some things we’ll want to do, that fetch can.

openai - For using the GPT-3.5-turbo model to answer the questions

We have a bit of context about this in the readme: https://github.com/lastmile-ai/aiconfig#what-problem-it-solv.... The main issue with keeping it in code is that it tangles application code with prompts and model-specific logic.

That makes it hard to evaluate the genAI parts of the application, and also iterating on the prompts is not as straightforward as opening up a playground.

Having the config be the source of truth let's you connect it to your application code (and still source controlled), lets you evaluate the config as the AI artifact, and also lets you open the config in a playground to edit and iterate.

For example, compare how much simpler openai function calling becomes with storing the stuff as a config: https://github.com/lastmile-ai/aiconfig/blob/main/cookbooks/... vs using vanilla openai directly (https://github.com/openai/openai-node/blob/v4/examples/funct...)

In your preferred serverless environment, make sure you install the OpenAI API Library and Vercel AI library to get started.

By following these steps, developers can train ChatGPT on their own data. This will allow to give personalized, accurate, and domain-specific responses. Keep in mind that this process requires technical skills and can take more time than using no-code platforms.

ChatGPT prompt: Act like a senior software developer mentor. Explain to me in the simplest way possible what javascript Symbols are, making very basic examples that DO NOT use "foo" "bar" words. Make small sentences and ask me often if I am able to understand. Thank you.

AI and LLMs such as ChatGPT are amazing at what they do, but they suffer from the lack of "an opposable thumb", to use an analogy. This implies that as stand alone products, they can't really do much, if anything at all.

Prompt Engineering is the art of instructing an LLM such as ChatGPT to do what you want it to do, using nothing but natural language, logic, and reason. The process is probably easily within reach of 80% of the world's population, while less than 0.3% of the world's population knows how to code.

ChatGPT, launched in November 2022, quickly became a sensation, amassing over a million users within its first five days. This staggering adoption rate signifies more than mere curiosity; it reflects a genuine interest in AI's potential to revolutionize writing. Built on the GPT (Generative Pre-trained Transformer) architecture, ChatGPT learns from a vast corpus of online text, enabling it to generate human-like responses across a wide range of topics and styles.

If you are new to OpenAI, create your OpenAI account here. Once you’ve successfully created an account, go on to API key section by using the same credentials you used when creating your account. On the left panel of the screen, click on “API Keys” and then proceed to click on “Create new secret key”. This is shown below:

A friend of mine asked ChatGPT to suggest an open source low-code software development platform for AI solutions, and it suggested Magic. Magic is our flagship product, and when I describe what it does I tend to use a lot of words. Below is the identity definition of it.

We'll use several interesting technologies to achieve this: Strapi CMS to take care of the content management and backend, Astro which is a great new technology for quickly creating blazing fast frontend apps, and ChatGPT to provide the article summaries.