decorator VS pydantic

that doesn't make much sense and there are necessary uses for eval() /exec(), mostly for dynamic creation of code:

For example here's Python dataclasses in the standard library using exec() to create the `__init__` and other methods that go on your dataclass:

https://github.com/python/cpython/blob/main/Lib/dataclasses....

Here's Pydantic using it for a jupyter notebook check:

https://github.com/pydantic/pydantic/blob/594effa279668bd955...

here's Pytest using it to rewrite modules so that functions like assert etc. are instrumented by pytest:

https://github.com/pytest-dev/pytest/blob/eca93db05b6c5ec101...

Here's the decorator module using it (as is the only way to do this in Python) to create a signature matching decorator for an arbitrary function:

https://github.com/micheles/decorator/blob/ad013a2c1ad796996...

All of these libraries are completely secure as eval/exec are used with code fragments that are generated by the libraries, not based on untrusted input.

eval() /exec() are not running executable files, just Python code, the same way all the rest of the package is already doing.

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.