certmagic VS pydantic

Now, I serve TLS directly from the application and was able to make it all work with Certmagic.

For deployment, you may not need a reverse proxy with Nginx or the likes. Certmagic will make HTTPS a breeze. Also makes it possible to handle multi-tenant SaaS domains SSL provisioning. While not the easiest, it was much easier than trying to do it at the reverse proxy and cheaper than doing it with Cloudflare's SaaS service.

If you don't want to move certificate management to a different service, use CertMagic in your app.

With certmagic its done completly automatic using letsencrypt: https://github.com/caddyserver/certmagic

The project is also a boon for devs. The certmagic library0 makes it trivial to add Let's Encrypt support to any Golang web server code.

Because Lego maintainers wouldn't budge when Caddy needed changes made to increase ACME reliability. Matt wrote his own implementation https://github.com/mholt/acmez and started using that in Caddy soon after. There's a deeper explanation here: https://github.com/caddyserver/certmagic/issues/71

That said, I would use https://github.com/caddyserver/certmagic to manage you SSL certs.

I terminate SSL in GO (less moving parts to manage), and use https://github.com/caddyserver/certmagic to provision and renew my certs.

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.