js_of_ocaml VS pydantic

In reality, most folks that developed BuckleScript frontends with ReasonML switched to ReScript syntax and are happy with it. Some felt more friction because of their reliance on PPXes or FP-heavy libraries (like Relude) and those people tend to use the Melange fork of BuckleScript or they switched to js_of_ocaml.

TL;DR: Reason is, and always has been, independent of ReScript (formerly BuckleScript) and is still actively maintained. If you want to use Reason or OCaml to make native code you can. If you want to compile JS from OCaml or Reason you also still can, but you'll want to use either js_of_ocaml or a BuckleScript fork named Melange that was made in response to the BuckleScript/ReScript people's shenanigans. And if you only care about JS-like syntax and JS output, you can use ReScript.

Personally, I just use OCaml and compile to JavaScript with Js_of_ocaml. Js_of_ocaml has been stable and actively maintained for a decade, and I don't have to worry if some superficial syntactic change that affects nothing but ASCII art is going to break my code.

The compilation to JavaScript is the sweet section of this blog post since we are using OCaml (under the hood while writing Reason), and it allows us to compile directly to JavaScript using js_of_ocaml.

First, note the method prefix_allowed_tokens_fn. This method applies a Pydantic model to constrain/guide how the LLM generates tokens. Next, see how that constrain can be applied to txtai's LLM pipeline.

utype is a concise alternative of pydantic with simplified parameters and usages, supporting both sync/async functions and generators parsing, and capable of using native logic operators to define logical types like AND/OR/NOT, also provides custom type parsing by register mechanism that supports libraries like pydantic, attrs and dataclasses

I like some of the changes from v1 to v2. But then you have something like this [0] removed from the library without proper documentation or replacement, resulting in ugly workarounds in the link that wont' work properly.

[0]: https://github.com/pydantic/pydantic/discussions/6337

The easiest implementation I found was to use a PyDantic class for my target schema — and use that as a parameter for the method call to “ChatCompletion.create()”. Here’s a fragment of the GinAI Python classes used.

Also, FastStream uses Pydantic to parse input JSON-encoded data into Python objects, making it easy to work with structured data in your applications, so you can serialize your input messages just using type annotations.

Pydantic Validation: Leverage Pydantic's validation capabilities to serialize and validate incoming messages

Not sure if it is exactly related, but there is an open issue on Github for that exact message.

Well the performance increase is so huge because pydantic1 is really really slow. And for using rust, I'd have expected more tbh…

I've been benchmarking pydantic v2 against typedload (which I write) and despite the rust, it still manages to be slower than pure python in some benchmarks.

The ones on the website are still about comparing to v1 because v2 was not out yet at the time of the last release.

pydantic's author will refuse to benchmark any library that is faster (https://github.com/pydantic/pydantic/pull/3264 https://github.com/pydantic/pydantic/pull/1525 https://github.com/pydantic/pydantic/pull/1810) and keep boasting about amazing performances.

On pypy, v2 beta was really really really slow.