OpenAPI-Specification
pydantic
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OpenAPI-Specification | pydantic | |
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44 | 167 | |
28,253 | 18,617 | |
1.2% | 4.3% | |
8.7 | 9.8 | |
1 day ago | 4 days ago | |
Markdown | 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
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
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Better Fastly API clients with OpenAPI Generator
The Fastly API is huge. We have lots of customers who want to interact with it using their chosen programming language but our small set of manually maintained clients was not sufficient to handle the job of our ever-evolving API. We needed a way to scale up our API client support, and OpenAPI was the answer.
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?
Cypress - Fast, easy and reliable testing for anything that runs in a browser.
Cerberus - Lightweight, extensible data validation library for Python
supertest - 🕷 Super-agent driven library for testing node.js HTTP servers using a fluent API. Maintained for @forwardemail, @ladjs, @spamscanner, @breejs, @cabinjs, and @lassjs.
nexe - 🎉 create a single executable out of your node.js apps
grpc-gateway - gRPC to JSON proxy generator following the gRPC HTTP spec
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
api-guidelines - Microsoft REST API Guidelines
SQLAlchemy - The Database Toolkit for Python
google.aip.dev - API Improvement Proposals. https://aip.dev/
sqlmodel - SQL databases in Python, designed for simplicity, compatibility, and robustness.
swagger-tools - A Node.js and browser module that provides tooling around Swagger.
mypy - Optional static typing for Python