datamodel-code-generator
zod
datamodel-code-generator | zod | |
---|---|---|
9 | 290 | |
2,315 | 30,477 | |
- | - | |
9.4 | 9.1 | |
5 days ago | 5 days ago | |
Python | TypeScript | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
datamodel-code-generator
- Datamodel-code-generator: Pydantic model/dataclass from OpenAPI, JSON, YAML
-
tRPC – Move Fast and Break Nothing. End-to-end typesafe APIs made easy
Like generating pydantic models or dataclasses for an OpenAPI schema? I haven't needed to go in that direction myself, but this[0] looks promising!
Apologies if I've misunderstood your comment
https://koxudaxi.github.io/datamodel-code-generator/
-
OpenAPI v4 Proposal
I'm sorry, but you have completely misunderstood the purpose of Open API.
It is not a specification to define your business logic classes and objects -- either client or server side. Its goal is to define the interface of an API, and to provide a single source of truth that requests and responses can be validated against. It contains everything you need to know to make requests to an API; code generation is nice to have (and I use it myself, but mainly on the server side, for routing and validation), but not something required or expected from OpenAPI
For what it's worth, my personal preferred workflow to build an API is as follows:
1. Build the OpenAPI spec first. A smaller spec could easily be done by hand, but I prefer using a design tool like Stoplight [0]; it has the best Web-based OpenAPI (and JSON Schema) editor I have encountered, and integrates with git nearly flawlessly.
2. Use an automated tool to generate the API code implementation. Again, a static generation tool such as datamodel-code-generator [1] (which generates Pydantic models) would suffice, but for Python I prefer the dynamic request routing and validation provided by pyapi-server [2].
3. Finally, I use automated testing tools such as schemathesis [3] to test the implementation against the specification.
[0] https://stoplight.io/
[1] https://koxudaxi.github.io/datamodel-code-generator/
[2] https://pyapi-server.readthedocs.io
[3] https://schemathesis.readthedocs.io
-
Create Pydantic datamodel from huge JSON file with local datamodel-code-generator
The site also provide a link to the github repo of the underlying program.
-
PSA: I think this JSON to Pydantic converter is extremely useful for boilerplate model creation
Not sure who owns/hosts the site, but its based on this github repo.
-
My top python library
That's what datamodel-code-generator propose.
-
I use attrs instead of pydantic
had generally good experience creating typed wrappers for api's with json-schema-to-pydantic[0] converter
[0] https://github.com/koxudaxi/datamodel-code-generator
-
What's the best libraries to build a REST API with Openapi compatibility
To save you some work, if you have already an OpenAPI specification at hand, you can use datamodel-code-generator to generate your Pydantic models from the spec.
-
This is what I pushed today, I don't know why but I was very positive about the code until someone reviewed it and pointed out the obvious. Also 'internal_data' field is very essential for other parts of the code. It is so embarrassing I want to disappear from the face of the earth.
And there are code generators for it! https://github.com/koxudaxi/datamodel-code-generator/
zod
-
Simplifying Form Validation with Zod and React Hook Form
[Zod Documentation](https://zod.dev/) [Zod Error Handling](https://zod.dev/ERROR_HANDLING?id=error-handling-in-zod) [React-Hook-Form Documentation](https://react-hook-form.com/get-started) [Hookform Resolvers](https://www.npmjs.com/package/@hookform/resolvers)
-
Figma's Journey to TypeScript
This is a very fair comment, and you seem open to understanding why types are useful.
"problems that are due to typing" is a very difficult thing to unpack because types can mean _so_ many things.
Static types are absolutely useless (and, really, a net negative) if you're not using them well.
Types don't help if you don't spend the time modeling with the type system. You can use the type system to your advantage to prevent invalid states from being represented _at all_.
As an example, consider a music player that keeps track of the current song and the current position in the song.
If you model this naively you might do something like: https://gist.github.com/shepherdjerred/d0f57c99bfd69cf9eada4...
In the example above you _are_ using types. It might not be obvious that some of these issues can be solved with stronger types, that is, you might say that "You rarely see problems that are due to typing".
Here's an example where the type system can give you a lot more safety: https://gist.github.com/shepherdjerred/0976bc9d86f0a19a75757...
You'll notice that this kind of safety is pretty limited. If you're going to write a music app, you'll probably need API calls, local storage, URL routes, etc.
TypeScript's typechecking ends at the "boundaries" of the type system, e.g. it cannot automatically typecheck your fetch or localStorage calls return the correct types. If you're casting, you're bypassing the type systems and making it worthless. Runtime type checking libraries like Zod [0] can take care of this for you and are able to typecheck at the boundaries of your app so that the type system can work _extremely_ well.
[0]: https://zod.dev/ note: I mentioned Zod because I like it. There are _many_ similar libraries.
-
From Flaky to Flawless: Angular API Response Management with Zod
Zod is an open-source schema declaration and validation library that emphasizes TypeScript. It can refer to any data type, from simple to complex. Zod eliminates duplicative type declarations by inferring static TypeScript types and allows easy composition of complex data structures from simpler ones. It has no dependencies, is compatible with Node.js and modern browsers, and has a concise, chainable interface. Zod is lightweight (8kb when zipped), immutable, with methods returning new instances. It encourages parsing over validation and is not limited to TypeScript but works well with JavaScript as well.
- TypeScript Essentials: Distinguishing Types with Branding
-
You can’t run away from runtime errors using TypeScript
Zod is a TypeScript-first schema declaration and validation library. It helps create schemas for any data type and is very developer-friendly. Zod has the functional approach of "parse, don't validate." It supports coercion in all primitive types.
-
Best Next.js Libraries and Tools in 2024
Link: https://zod.dev/
-
Popular Libraries For Building Type-safe Web Application APIs
You can check out their documentation here.
-
Epic Next JS 14 Tutorial Part 4: How To Handle Login And Authentication in Next.js
You can learn more about Zod on their website here.
-
What even is a JSON number?
In JS, it's a good idea anyway to use some JSON parsing library instead of JSON.parse.
With Zod, you can use z.bigint() parser. If you take the "parse any JSON" snippet https://zod.dev/?id=json-type and change z.number() to z.bigint(), it should do what you are looking for.
-
Error handling in our form component for the NextAuth CredentialsProvider
We will validate our input using client-side zod. Zod handles TypeScript-first schema validation with static type inference. This means that it will not only validate your fields, it will also set types on validated fields.
What are some alternatives?
sqlmodel - SQL databases in Python, designed for simplicity, compatibility, and robustness.
class-validator - Decorator-based property validation for classes.
pydantic - Data validation using Python type hints
joi - The most powerful data validation library for JS [Moved to: https://github.com/sideway/joi]
pydantic-factories - Simple and powerful mock data generation using pydantic or dataclasses
typebox - Json Schema Type Builder with Static Type Resolution for TypeScript
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
Yup - Dead simple Object schema validation
odmantic - Sync and Async ODM (Object Document Mapper) for MongoDB based on python type hints
ajv - The fastest JSON schema Validator. Supports JSON Schema draft-04/06/07/2019-09/2020-12 and JSON Type Definition (RFC8927)
cattrs - Composable custom class converters for attrs.
io-ts - Runtime type system for IO decoding/encoding