LaTeXML
pydantic
LaTeXML | pydantic | |
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3 | 167 | |
850 | 18,942 | |
- | 3.8% | |
8.7 | 9.8 | |
7 days ago | about 21 hours ago | |
Perl | Python | |
GNU General Public License v3.0 or later | MIT License |
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LaTeXML
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Wikipedia of Algebraic Geometry Will Forever Be Incomplete. (2022)
Stacks project is available on github, so in theory (if you're bored enough) it should be possible to reverse engineer their design from their make-project file https://github.com/stacks/stacks-project/blob/master/documen...
At a high level they use plastex https://github.com/plastex/plastex to convert latex to html (you seem to be using pandoc?) and so can control the rendering to any fine accuracy they want. I liked this general style as well, so I tried using plastex but couldn't get my head around it and so started using LateXML https://github.com/brucemiller/LaTeXML
My usecase: I wanted to have a "dependency graph" of lemmas to make it easier to see proofs without having to jump back and forth through a pdf, and this was sort of similar to lean formalization blueprint graphs https://teorth.github.io/pfr/blueprint/dep_graph_document.ht... (which also uses plastex) but without the lean parts. There's still a lot of work to be done, but I think I have a pretty okay implementation using latexml which meets 50% of my requirements for now, so I'm happyish https://texviz.arsricharan.in/ghrss24/
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I don't always use LaTeX, but when I do, I compile to HTML (2013)
LaTeML [1] is presumably the latex to html tool that arXiv is testing right now. What are peoples thoughts about it compared to other such tools?
[1] https://github.com/brucemiller/LaTeXML
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Python Type Hints Are Turing Complete
They aren't using the rendered PDFs. They are convering from the LaTeX sources, that you upload to arxiv with https://github.com/brucemiller/LaTeXML
pydantic
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Advanced RAG with guided generation
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.
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utype VS pydantic - a user suggested alternative
2 projects | 15 Feb 2024
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
- Pydantic v2 ruined the elegance of Pydantic v1
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Ask HN: Pydantic has too much deprecation. Why is it popular?
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
- OpenAI uses Pydantic for their ChatCompletions API
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🍹GinAI - Cocktails mixed with generative AI
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.
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FastStream: Python's framework for Efficient Message Queue Handling
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.
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Introducing FastStream: the easiest way to write microservices for Apache Kafka and RabbitMQ in Python
Pydantic Validation: Leverage Pydantic's validation capabilities to serialize and validate incoming messages
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Cannot get Langchain to work
Not sure if it is exactly related, but there is an open issue on Github for that exact message.
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FastAPI 0.100.0:Release Notes
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.
What are some alternatives?
python-typing-machines - Python type hints are Turing complete.
Cerberus - Lightweight, extensible data validation library for Python
xlcalculator - xlcalculator converts MS Excel formulas to Python and evaluates them.
nexe - 🎉 create a single executable out of your node.js apps
di - Pythonic dependency injection
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
json-parser-in-typescript-ver
SQLAlchemy - The Database Toolkit for Python
plain_latex_book - A plain latex book template
sqlmodel - SQL databases in Python, designed for simplicity, compatibility, and robustness.
Asciidoctor - :gem: A fast, open source text processor and publishing toolchain, written in Ruby, for converting AsciiDoc content to HTML 5, DocBook 5, and other formats.
mypy - Optional static typing for Python