enforce
pydantic
enforce | pydantic | |
---|---|---|
3 | 167 | |
541 | 18,733 | |
- | 2.1% | |
0.0 | 9.8 | |
about 2 years ago | 1 day ago | |
Python | Python | |
- | MIT License |
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enforce
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Python’s “Type Hints” are a bit of a disappointment to me
Every point in this blog post strikes me as either (1) unaware of the tooling around python typing other than mypy, or (2) a criticism of static-typing-bolted-on-to-a-dynamically-typed-language, rather than Python's hints. Regarding (1), my advise to OP is to try out Pyright, Pydantic, and Typeguard. Pyright, especailly, is amazing and makes the process of working with type hints 2 or 3 times smoother IMO. And, I don't think points that fall under (2) are fair criticisms of type *hints*. They are called hints for a reason.
Otherwise, here's a point-by-point response, either recommending OP checks out tooling, or showing that the point being made is not specific to Python.
> type hints are not binding.
There are projects [0][1] that allow you to enforce type hints at runtime if you so choose.
It's worth mentioning that this is very analogous to how Typescript does it, in that type info is erased completely at runtime.
> Type checking is your job after all, ...[and that] requires maintenance.
There are LSPs like Pyright[2] (pyright specifically is the absolute best, IMO) that report type errors as you code. Again, this is very very similar to typescript.
> There is an Any type and it renders everything useless
I have never seen a static-typing tool that was bolted on to a dynamically typed language, without an `Any` type, including typescript.
> Duck type compatibility of int and float
The author admits that they cannot state why this behavior is problematic, except for saying that it's "ambiguous".
> Most projects need third-party type hints
Again, this is a criticism of all cases where static types are bolted on dynamically typed languages, not Python's implementation specifically.
> Sadly, dataclasses ignore type hints as well
Pydantic[3] is an amazing data parsing library that takes advantage of type hints, and it's interface is a superset of that of dataclasses. What's more, it underpins FastAPI[4], an amazing API-backend framework (with 44K Github stars).
> Type inference and lazy programmers
The argument of this section boils down to using `Any` as a generic argument not being an error by default. This is configurable to be an error both in Pyright[5], and mypy[6].
> Exceptions are not covered [like Java]
I can't find the interview/presentation, but Guido Van Rossum specifically calls out Java's implementation of "exception annotations" as a demonstration of why that is a bad idea, and that it would never happen in Python. I'm not saying Guido's opinion is the absolute truth, but just letting you know that this is an explicit decision, not an unwanted shortcoming.
[0] https://github.com/RussBaz/enforce
[1] https://github.com/agronholm/typeguard
[2] https://github.com/microsoft/pyright
[3] https://pydantic-docs.helpmanual.io
[4] https://github.com/tiangolo/fastapi
[5] https://github.com/microsoft/pyright/blob/main/docs/configur...
[6] https://mypy.readthedocs.io/en/stable/config_file.html#confv...
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Unit tests & type hinting
Not by default. But there are libraries to enforce types. https://github.com/RussBaz/enforce or/and https://pydantic-docs.helpmanual.io/
- Type validation decorator
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?
pydantic-to-typescript - CLI Tool for converting pydantic models into typescript definitions
Cerberus - Lightweight, extensible data validation library for Python
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
nexe - 🎉 create a single executable out of your node.js apps
streamlit-pydantic - 🪄 Auto-generate Streamlit UI from Pydantic Models and Dataclasses.
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
libsa4py - LibSA4Py: Light-weight static analysis for extracting type hints and features
SQLAlchemy - The Database Toolkit for Python
pyright - Static Type Checker for Python
sqlmodel - SQL databases in Python, designed for simplicity, compatibility, and robustness.
returnn - The RWTH extensible training framework for universal recurrent neural networks
mypy - Optional static typing for Python