Paste JSON as Code • quicktype VS pydantic

Quicktype automates the generation of code from JSON data. It's a real timesaver when dealing with complex JSON structures in your applications.

Nice! JSON schemas are really useful, we use them a lot for code generation. Another library that does this for multiple languages is https://quicktype.io/ . It's great, but not so actively developed.

Quicktype is a speedy way to transform JSON data into well-structured, type-safe code in any programming language. Can help simplify the process of generating models and serializers from JSON, schema, or GraphQL—for secure, efficient data handling. 6-20PM finds it a "Godsend to convert JSON into data structures in any language."

Good candidate modules for these initial conversions are utility functions and code related to external APIs or specifications. It is possible to automatically generate TypeScript type definitions from Swagger contracts, GraphQL or JSON schemas to be included in your project. as well as you can use website called https://quicktype.io/ to generate types and interfaces in order to user them in your tsx or ts file.

Using Codable structs is better than dictionaries. Paste the JSON into https://quicktype.io/ and it will create the structs for you.

May be quicktype.io?

Check out quicktype.io. You can paste in your JSON and it will build your model for you. There's even an option for creating constructors. And you can choose your programming language. I usually spend some time cleaning it up a bit. It's not the perfect solution and I don't use it all the time, but it sure saves time for complicated models.

Hello. I used quicktype.io to try and implement an api in my code.

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.