TUI-apps VS pydantic

Hello!

I am pleased to announce a new version of my "CLI text processing with GNU sed" ebook. This book heavily leans on examples to present features one by one. In addition to sed commands and options, regular expressions are also discussed in detail.

Links:

* PDF/EPUB versions: https://learnbyexample.gumroad.com/l/gnu_sed (free for a few days)

* Web version: https://learnbyexample.github.io/learn_gnused/ (always free)

* Markdown source, example files, etc: https://github.com/learnbyexample/learn_gnused

* Interactive TUI app for exercises: https://github.com/learnbyexample/TUI-apps/blob/main/SedExercises

I would highly appreciate it if you'd let me know how you felt about this book. It could be anything from a simple thank you, pointing out a typo, mistakes in code snippets, which aspects of the book worked for you (or didn't!) and so on. Reader feedback is essential and especially so for self-published authors.

Happy learning :)

I also wrote an interactive TUI app based on some of the exercises from the ebook. Reference solutions are provided for both GNU grep and ripgrep.

These days I prefer coding TUI apps using Textual (see my repo https://github.com/learnbyexample/TUI-apps for examples) since I find this framework easier to work with and I don't need full GUI features for my projects.

This year I started updating my existing books and I'm also creating interactive apps. Here's one for Python regex: https://github.com/learnbyexample/TUI-apps/blob/main/PyRegexExercises. Next is grep.

Having an interactive program that automatically loads questions and checks the solution is wonderful to have while learning a topic. I wrote a TUI app that has beginner to advanced level exercises for Python regular expressions. There are more than 100 exercises covering both the builtin re and third-party regex modules.

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.