pyre-check VS pydantic

Did you come across pyre in your search? MIT license and pretty fast.

https://github.com/facebook/pyre-check

Pyre is a performant type-checker developed by Facebook. Pyre can analyse codebases with millions of lines of code incrementally – providing instantaneous feedback to developers as they write code.

Pyre from Meta, pyright from Microsoft and PyType from Google provide additional assistance. They can 'infer' types based on code flow and existing types within the code.

Have you seen Pyre[0]? Not Rust, OCaml, and pretty fast. Made by a team at Meta and open sourced on GitHub. If you use python-lsp, I wrote an extension[1] to enable integration (though I haven't tested it recently, been programming in rust; it is mostly a "for me" extension).

0: https://pyre-check.org/

1: https://github.com/cricalix/python-lsp-pyre

Internally we use Pyre for Python type checking: https://github.com/facebook/pyre-check

Before type hinting, work had intense rules and linters enforcing docstrings with types. Now, type hints and automatic pyre runs take care of all the heavy lifting.

Python now has an optional type system and if you add one of them such as mypy or pyre to your CI process and you can configure GitHub to refuse the pull request until types are added you can make it somewhat strongly typed.

If you have a preexisting codebase I believe the way you can convert it is to add the types that you know on commits and eventually you will have enough types that adding the missing ones should be easy. For the missing ones Any is a good choice.

https://pyre-check.org and https://github.com/python/mypy are popular.

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.