jsonformer VS zod

- Tools like jsonformer https://github.com/1rgs/jsonformer are not possible with OpenAIs API.

How does this compare in terms of latency, cost, and effectiveness to jsonformer? https://github.com/1rgs/jsonformer

You're correct with interpreting how the model works wrt it returning tokens one at a time. The model returns one token, and the entire context window gets shifted right by one to for account it when generating the next one.

As for model performance at different context sizes, it's seems a bit complicated. From what I understand, even if models are tweaked (for example using the superHOT RoPE hack or sparse attention) to be able to use longer contexts, they still have to be fined tuned on input of this increased context to actually utilize it, but performance seems to degrade regardless as input length increases.

For your question about fine tuning models to respond with only "yes" or "no", I recommend looking into how the jsonformers library works: https://github.com/1rgs/jsonformer . Essentially, you still let the model generate many tokens for the next position, and only accept the ones that satisfy certain criteria (such as the token for "yes" and the token for "no".

You can do this with openAI API too, using tiktoken https://twitter.com/AAAzzam/status/1669753722828730378?t=d_W... . Be careful though as results will be different on different selections of tokens, as "YES", "Yes", "yes", etc are all different tokens to the best of my knowledge

From here, we just need to continue generating tokens until we get to a closing quote. This approach was borrowed from Jsonformer which uses a similar approach to induce LLMs to generate structured output. Continuing to do so for each property using Replit's code LLM gives the following output:

https://github.com/1rgs/jsonformer or https://github.com/microsoft/guidance may help get better results, but I ended up with a bit more of a custom solution.

I think function calling is just JSONformer idk: https://github.com/1rgs/jsonformer

jsonformer: https://github.com/1rgs/jsonformer

Zod TypeScript-first schema validation with static type inference

Zod

To define the action's inputs and outputs we will need the zod library. Zod is an npm package and ValTown lets us import it directly into our Vals:

There's libraries can do runtime checks for javascript, one of the most popular libraries do this is zod you can give it a try.

Zod is a validation library which will easily produce TypeScript typings for defined schemas.

What if you use a schema library, like Zod or Valibot? The schema has been built on value-level, and you cannot assign the variant to its property inside the declaration.

[Zod Documentation](https://zod.dev/) [Zod Error Handling](https://zod.dev/ERROR_HANDLING?id=error-handling-in-zod) [React-Hook-Form Documentation](https://react-hook-form.com/get-started) [Hookform Resolvers](https://www.npmjs.com/package/@hookform/resolvers)

This is a very fair comment, and you seem open to understanding why types are useful.

"problems that are due to typing" is a very difficult thing to unpack because types can mean _so_ many things.

Static types are absolutely useless (and, really, a net negative) if you're not using them well.

Types don't help if you don't spend the time modeling with the type system. You can use the type system to your advantage to prevent invalid states from being represented _at all_.

As an example, consider a music player that keeps track of the current song and the current position in the song.

If you model this naively you might do something like: https://gist.github.com/shepherdjerred/d0f57c99bfd69cf9eada4...

In the example above you _are_ using types. It might not be obvious that some of these issues can be solved with stronger types, that is, you might say that "You rarely see problems that are due to typing".

Here's an example where the type system can give you a lot more safety: https://gist.github.com/shepherdjerred/0976bc9d86f0a19a75757...

You'll notice that this kind of safety is pretty limited. If you're going to write a music app, you'll probably need API calls, local storage, URL routes, etc.

TypeScript's typechecking ends at the "boundaries" of the type system, e.g. it cannot automatically typecheck your fetch or localStorage calls return the correct types. If you're casting, you're bypassing the type systems and making it worthless. Runtime type checking libraries like Zod [0] can take care of this for you and are able to typecheck at the boundaries of your app so that the type system can work _extremely_ well.

[0]: https://zod.dev/ note: I mentioned Zod because I like it. There are _many_ similar libraries.

Zod is an open-source schema declaration and validation library that emphasizes TypeScript. It can refer to any data type, from simple to complex. Zod eliminates duplicative type declarations by inferring static TypeScript types and allows easy composition of complex data structures from simpler ones. It has no dependencies, is compatible with Node.js and modern browsers, and has a concise, chainable interface. Zod is lightweight (8kb when zipped), immutable, with methods returning new instances. It encourages parsing over validation and is not limited to TypeScript but works well with JavaScript as well.