mkdocs-material VS pydantic

Material Mk-Docs by Martin Donath works well if you prefer python.

https://squidfunk.github.io/mkdocs-material/

I'm an 'outsider', but from from the outside the Material For MkDocs Project looks like a very well managed open source project.

Martin Donath's project uses a 'sponsorware' release strategy to generate donations.

From my vantage point it seems to be working pretty well.

I just recently went through this decision process. My aim is to write code and math oriented posts so I need good support for nice syntax highlighting (at least colored) and mathjax (preferable) or katex. Substack is the most popular newsletter platform but fails at these two criteria. I love how math and syntax highlighting (plus numerous other features) work in MkDocs Material, which recently added a Blog plugin.

I wanted to combine the best of both: Substack as an amazing email social network, and MkDocs Material’s awesome look. So I’ve gone with using Substack as the core platform which I use to manage subscribers, and use it to post either math/code-free posts or a short teasers pointing to my main blog site on MkDocs Material when I need to show math/code

https://squidfunk.github.io/mkdocs-material/

I've been migrating content to MKDocs (Material) over the last few months, so feel fairly qualified on this subject. It's somewhat limited in terms of navigation, but can probably handle 400-500 pages; you can see how navigation works in the link. Otherwise, it can handle most, if not all, the tasks you've listed.

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.