uuid VS pydantic

https://github.com/gofrs/uuid upto v5

I use Google's UUID package a lot. But every time I refer to it in a new package, the language server picks up https://github.com/gofrs/uuid instead of https://github.com/google/uuid and then complains that the gofrs package isn't in go.mod. I assume because it's the first alphabetically (though this seems like a huge supply chain security loophole).

What makes this different than https://github.com/gofrs/uuid ?

Something to note is that all of this is still open source. Theoretically, someone can decide to fork SQLBoiler and add all the missing things, or send in a PR. A good example is that the current most popular uuid package. gofrs/uuid was forked from an unmaintained previously popular package.

You could also look at github.com/gofrs/uuid which includes support for v6 and v7 from the latest draft UUID spec. Personally I think the API for this library is nicer since the google one makes it difficult to tell what kind of UUID you are generating.

Postgres has native UUID support so don't use VARCHAR(36). Look at https://github.com/gofrs/uuid library, among others, to create uuid.

If you're using this library and are unsure what to do, a few of us maintain a fork of this library that has fixed these issues (and others): https://github.com/gofrs/uuid

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.