snakemake VS pydantic

Last year, my pull requests were on my own projects: two to for my Apple Watch telemetry recording app, one was for a workout Watch app, and the third was for the research project I was working on at the moment (this paper has since been published, "The origins and genetic interactions of KRAS mutations are allele- and tissue-specific" and the code is open source). This year, in order to enhance my learning, I challenged myself to contribute to others' projects. In my opinion, I have been quite successful with contributions to Fig, tldr, snakemake, and pymc3-examples. In addition, I have also taken up the challenge of learning about Appwrite, one of the sponsors of this year's Hacktoberfest, and producing educational content about the service. (With some encouragement by the offer of free stickers 🙃) I found this a great opportunity to learn about an essential world of programming that I had yet to deal with: backend services.

If you’re into data science, have you taken a look at Snakemake? I came across this quite recently, and now, I’m wondering why I hadn’t used it before (it really would’ve come in handy to automate a few data-wrangling processes).

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.