openapi-python-client
fastapi
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openapi-python-client | fastapi | |
---|---|---|
6 | 466 | |
1,066 | 71,023 | |
6.8% | - | |
9.0 | 9.8 | |
5 days ago | 1 day ago | |
Python | Python | |
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.
openapi-python-client
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GraphQL is for Backend Engineers
On the backend, developers either need to manually document the entire API or rely on auto-generation tools that don’t fully meet their needs. Consumers face the same choice, write code by hand or workaround the bugs in their SDK generator (stated, lovingly, as the maintainer of an OpenAPI client generator). On top of this, these solutions result in inconsistent understandings of the API. Reproducing errors becomes time-consuming and frustrating, which feels like a battle instead of a collaboration. What we need is a shared language to describe how the API works—one that doesn’t add unnecessary layers of abstraction or manual work.
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Microsoft Kiota: CLI for generating an API client to call OpenAPI-described API
Has anyone tried Kiota, specifically the Python support? How does it compare to https://github.com/openapi-generators/openapi-python-client ?
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Python toolkits
I think we use these - https://github.com/openapi-generators/openapi-python-client
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YAML: It's Time to Move On
Thanks for the link, but not necessarily.
How WSDL and the code generation around it worked, was that you'd have a specification of the web API (much like OpenAPI attempts to do), which you could feed into any number of code generators, to get output code which has no coupling to the actual generator at runtime, whereas Pyotr is geared more towards validation and goes into the opposite direction: https://pyotr.readthedocs.io/en/latest/client/
The best analogy that i can think of is how you can also do schema first application development - you do your SQL migrations (ideally in an automated way as well) and then just run a command locally to generate all of the data access classes and/or models for your database tables within your application. That way, you save your time for 80% of the boring and repetitive stuff while minimizing the risks of human error and inconsistencies, while nothing preventing you from altering the generated code if you have specific needs (outside of needing to make it non overrideable, for example, a child class of a generated class). Of course, there's no reason why this can't be applied to server code either - write the spec first and generate stubs for endpoints that you'll just fill out.
Similarly there shouldn't be a need for a special client to generate stubs for OpenAPI, the closest that Python in particular has for now is this https://github.com/openapi-generators/openapi-python-client
However, for some reason, model driven development never really took off, outside of niche frameworks, like JHipster: https://www.jhipster.tech/
Furthermore, for whatever reason formal specs for REST APIs also never really got popular and aren't regarded as the standard, which to me seems silly: every bit of client code that you write will need a specific version to work against, which should be formalized.
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Replacing FastAPI with Rust: Part 2 - Research
Tallying up the results, we get 7/8 "MUST" requirements met. I think that Paperclip + actix-web seems like the most promising candidate. I'm really not opposed to writing the OpenAPI v3 construction myself as I've worked with the structure a fair bit in my openapi-python-client project (shameless plug).
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Replacing FastAPI with Rust: Part 1 - Intro
Automatic documentation via OpenAPI, which lets you do things like generate Python code that knows how to talk to your API.
fastapi
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Building an Email Assistant Application with Burr
In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.
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FastAPI Got Me an OpenAPI Spec Really... Fast
That’s when I found FastAPI.
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How to Deploy a Fast API Application to a Kubernetes Cluster using Podman and Minikube
FastAPI & Uvicorn
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Analysing FastAPI Middleware Performance
Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696
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LangChain, Python, and Heroku
An API application framework (such as FastAPI)
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Litestar – powerful, flexible, and highly performant Python ASGI framework
It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.
This ends up made worse if you’re using sync routes.
If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.
This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759
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AI-Powered Image Search with CLIP, pgvector, and Fast API
Fast API.
- Ask HN: What is your go-to stack for the web?
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Fun with Avatars: Crafting the core engine | Part. 1
We will create our API using FastAPI, a modern high-performance web framework for building fast APIs with Python. It is designed to be easy to use, efficient, and highly scalable. Some key features of FastAPI include:
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Building Fast APIs with FastAPI: A Comprehensive Guide
FastAPI is a modern, fast, web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use, fast to run, and secure. In this blog post, we’ll explore the key features of FastAPI and walk through the process of creating a simple API using this powerful framework.
What are some alternatives?
sqlx - 🧰 The Rust SQL Toolkit. An async, pure Rust SQL crate featuring compile-time checked queries without a DSL. Supports PostgreSQL, MySQL, and SQLite.
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
starlark - Starlark Language
HS-Sanic - Async Python 3.6+ web server/framework | Build fast. Run fast. [Moved to: https://github.com/sanic-org/sanic]
paperclip - WIP OpenAPI tooling for Rust. [Moved to: https://github.com/paperclip-rs/paperclip]
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.
okapi - OpenAPI (AKA Swagger) document generation for Rust projects
django-ninja - 💨 Fast, Async-ready, Openapi, type hints based framework for building APIs
warp - A super-easy, composable, web server framework for warp speeds.
Flask - The Python micro framework for building web applications.
yaml-reference-parser
swagger-ui - Swagger UI is a collection of HTML, JavaScript, and CSS assets that dynamically generate beautiful documentation from a Swagger-compliant API.