dataclasses VS pydantic

dataclasses was explicitly inspired by attrs. https://github.com/ericvsmith/dataclasses/issues/1

I have only just encountered this expression — the “!r” appended to a variable or class attribute, as in the following example from the Dataclasses backports repo for Python 3.6:

Pydantic Settings is a python library that extends 🚀 Pydantic for dealing with settings management.

And here is where FastAPI ✨ shines: It leverages Pydantic powerful data validation to offer out of the box JSON Schema and OpenAPI specs via Swagger and Redoc.

If you carefully think about it, the system that extracts raw text from the PDF needs to both detect and render PDF form elements like checkboxes and radiobuttons in a way that LLMs can understand. In this example, we’ll use LLMWhisperer to extract PDF raw text representing checkboxes and radiobuttons. You can use LLMWhisperer completely free for processing up to 100 pages per day. As for structuring the output from LLMWhisperer, we’ll use GPT3.5-Turbo and we’ll use Langchain and Pydantic to help make our job easy.

Pydantic: Use Pydantic to declare your data model. This output parser allows users to specify an arbitrary Pydantic Model and query LLMs for outputs that conform to that schema.

1. Pydantic: Ultimate Data Validation

I'm really surprised ijl got angry that his mail was quoted, it looks innocent enough to me.

For reference it's been edited out here: https://github.com/pydantic/pydantic/issues/589

But github shows edits, so the edit is meaningless for privacy. Here's the original mail (yes, I'm blatantly ignoring his request to not publish this, I'm just this evil.)

    I've looked into replacing ujson in pydantic with orjson

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