asgiref VS pydantic

uvicorn is an ASGI server that is recommended for running FastAPI applications.

I can't seem to be able to edit on mobile. OP either meant this, or its variation, such as async_to_sync and sync_to_async.

https://github.com/django/asgiref/blob/main/asgiref/sync.py

Ofc this is a python example. I have no idea how it works in different languages.

FastAPI is a popular Python Web framework that developers use to create RESTful APIs. It is based on Pydantic and Python-type hints that assist in the serialization, deserialization, and validation of data. In this tutorial, we will use FastAPI to create a simple "Hello World" application. We test and run the application locally. FastAPI requires a ASGI server to run the application production such as Uvicorn.

Look at the intended semantics [1], and then read the implementation [2]. Can you figure out if the implementation is correct? Can you infer the possible limitations of the approach at glance? Can your async library actually handle being called with multiple event loops installed?

I have zero trust in this code and I have been bitten by fixes to this library that introduced deadlocks in my own code.

[1] https://github.com/django/asgiref#synchronous-code--threads.

[2] https://github.com/django/asgiref/blob/main/asgiref/sync.py#...

Using WebSockets in Django utilizes asynchronous Python and Django channels, making the process straightforward. Using Django channels, you can create an ASGI server, and then create a group where users can send text messages to all the other users in the group in real time. This way, you are not communicating with a particular user, but with a group, multiple users can be added.

If you're interested in the architecture itself I recommend you start by making yourself familiar with [ASGI specification](https://asgi.readthedocs.io/en/latest/) .

We switched to using strong typing derived from the asgiref for typing ASGI types, which makes Starlite the strongest type framework of its kind.

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.