OpenAPI-Specification
pydantic
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OpenAPI-Specification | pydantic | |
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44 | 166 | |
28,047 | 18,226 | |
1.0% | 4.2% | |
8.6 | 9.8 | |
5 days ago | about 22 hours ago | |
JavaScript | Python | |
Apache License 2.0 | MIT License |
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OpenAPI-Specification
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Writing type safe API clients in TypeScript
And I'll be using the OpenAPI Pet Store spec file as an example.
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Show HN: OpenAPI DevTools – Chrome ext. that generates an API spec as you browse
I saw your sibling comment about "keeping it simple," however that is a bit counter to "generates OpenAPI specifications" since those for sure are not limited to just application/json request/response bodies
I wanted to draw your attention to "normal" POST application/x-www-form-urlencoded <https://github.com/OAI/OpenAPI-Specification/blob/3.1.0/vers...> and its multipart/form-data friend <https://github.com/OAI/OpenAPI-Specification/blob/3.1.0/vers...>
The latter is likely problematic, but the former is in wide use still, including, strangely enough, the AWS API, although some of their newer services do have an application/json protocol
I know that's a lot of words, but the tl;dr would be that if you want your extension to be application/json only, then changing the description to say "OpenAPI specifications for application/json handshakes" would help the consumer be on the same page with your goals
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How to Connect a FastAPI Server to PostgreSQL and Deploy on GCP Cloud Run
Since FastAPI is based on OpenAPI, at this point you can also use the automatically generated docs. There are multiple options, and two are included by default. Try them out by accessing the following URLs:
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Write a scalable OpenAPI specification for a Node.js API
This approach requires a constant context switch and is clearly not productive. Here, the OpenAPI Specification can help; you might already have it, but is it scalable? In this article, we’ll learn how to create an OpenAPI Specification document that is readable, scalable, and follows the principle of extension without modifying the existing document.
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OpenAPI 3.1 - The Gnarly Bits
As part of this release, we have decided to not follow SemVer anymore, and as such allow ourselves to introduce minor, but breaking changes. These changes are documented as part of the release notes.
Phil Sturgeon, who along with Ben Hutton and Henry Andrews from the JSON Schema community, helped drive the push to full JSON Schema Draft 2020-12 compliance, has written a blog post for the official OpenAPIs.org website on how to transition your OAS documents from v3.0.x to v3.1.0.
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Documenting Node.js API using Swagger
In this article, we will be learning how to document API written in Node.js using a tool called Swagger. Swagger allows you to describe the structure of your APIs so that machines can read them. The ability of APIs to describe their own structure is the root of all awesomeness in Swagger. Why is it so great? Well, by reading our API’s structure, swagger can automatically build beautiful and interactive API documentation. It can also automatically generate client libraries for your API in many languages and explore other possibilities like automated testing. Swagger does this by asking our API to return a YAML or JSON that contains a detailed description of your entire API. This file is essentially a resource listing of our API which adheres to OpenAPI Specifications.
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Getting started with REST APIs
You may encounter APIs described as RESTful that do not meet these criteria. This is often the result of bottom-up coding, where top-down design should have been used. Another thing to watch out for is the absence of a schema. There are alternatives, but OpenAPI is a common choice with good tools support. If you don't have a schema, you can create one by building a Postman collection.
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Automatic request validation at the edge with OpenAPI and Fastly
The principle behind the OpenAPI Specification (OAS – the industry’s most popular API specification format) is similar. It’s supposed to act as a blueprint for describing RESTful APIs.
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How would I describe a webhook, as part of my API collection?
OpenAPI 3.1 supports webhooks. It's not widely supported yet by implementations, but it's definitely there. https://github.com/OAI/OpenAPI-Specification/blob/main/examples/v3.1/webhook-example.yaml
pydantic
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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
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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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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.
- Pydantic 2.0
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[DISCUSSION] What's your favorite Python library, and how has it helped you in your projects?
As for the most utilized and still loved library, that would probably be pydantic, it helps declaring types so convenient - be it dto's, models or just complex arguments - and plays nice with bunch of other libraries from it's own ecosystem.
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popularity behind pydantic
I did read this ... Pydantic Docs.
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Guide to Serverless & Lambda Testing — Part 2 — Testing Pyramid
Schema validations logic — I use Pydantic for input validation and schema validation (boto responses, API responses, input validation, etc.) use cases. The Pydantic schema can contain type and value constraint checks or even more complicated logic with the custom validator code.
What are some alternatives?
Cerberus - Lightweight, extensible data validation library for Python
nexe - 🎉 create a single executable out of your node.js apps
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
SQLAlchemy - The Database Toolkit for Python
sqlmodel - SQL databases in Python, designed for simplicity, compatibility, and robustness.
mypy - Optional static typing for Python
pyparsing - Python library for creating PEG parsers [Moved to: https://github.com/pyparsing/pyparsing]
phonenumbers - Python port of Google's libphonenumber
dacite - Simple creation of data classes from dictionaries.
Lark - Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity.
beanie - Asynchronous Python ODM for MongoDB
beartype - Unbearably fast near-real-time hybrid runtime-static type-checking in pure Python.