requests-html VS pydantic

requests-html

Ya, thanks for advice. When using requests_html library, I am trying to lower down the speed using response.html.render(timeout=1000), but it raise Runtime error instead on Google Colab: https://github.com/psf/requests-html/issues/517.

For dynamic js, prefer requests-html with xpath selection.

Example: requests-html which has a rather exhaustive README.md, but their dedicated page is not that helpful, if I remember correctly, and currently the domain is suspended.

When it comes to web scraping, the usual people recommend is beautifulsoup, lxml, or selenium. But I highly recommend people check out requests-html also. Its a library that is a happy medium between ease of use as in beautifulsoup and also good enough to be used for dynamic, javascript data where it would be overkill to use a browser emulator like selenium.

Quite possible. If you look at requests-html source code, it is simply one single python file that acts as a wrapper around a bunch of other packages, like requests, chromium, parse, lxml, etc., plus a couple convenience functions. So it could easily be some sort of bad dependency resolution.

What I do is try to see if I can use requests_html first before trying selenium. requests_html is usually enough if I dont need to interact with browser widgets or if the authentication isnt too difficult to reverse engineer.

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.