Bottle VS pydantic

Bottle.py: uber-fast and simple python web microframework, about 3x faster, saner, and more memory-efficient than Flask in my experience: https://github.com/bottlepy/bottle

Fossil: distributed version control and much more in a single executable, from the creators of SQLite: https://fossil-scm.org/

This walkthrough I am using as learning material says the vulnerable code (in the first snippet below, from github here: https://github.com/bottlepy/bottle/issues/900), is replicated in the webpage in the second snippet below and so I know this app is vulnerable to the exploit shown in highlighted in the github link.

Here's a web framework in a single file: bottle.py

I don't do much development for microcontrollers or limited resource environments, but it's nice to have a low boilerplate tool for throwing together quick web apps. I've used Bottle for that in the past. It looks like this might have more of an API focus, rather than templates or static pages? Very cool, will check it out.

However much makes sense for your project. bottle.py is a web framework in a single file, and it's about 4500 lines of code. Should you do the same thing? Probably not. But you can.

There was a time, I want to start a new project and have to choose what technologies to use for the frontend and backend parts as well. Research gets me to Aqueduct and Shelf, both of them weren't looking actively developing and supported and that leads me to the idea to make my own small micro-framework like Echo for Golang or Bottle for Python. And it was easy to decide: I've had time and motivation :)

It was developed by Marcel Hellkamp and Bottle was initially released on July 1 2009 it is cross-platform and open-source. its Github repository is https://github.com/bottlepy/bottle. It is integrated with Python, Vue JS, and Jinja.

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.