awesome-jsonschema
pydantic
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awesome-jsonschema | pydantic | |
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70 | 167 | |
101 | 18,617 | |
- | 4.3% | |
5.3 | 9.8 | |
8 months ago | 1 day ago | |
Handlebars | Python | |
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awesome-jsonschema
- YAML or JSON files that are typed?
- Parse, Don't Validate (2019)
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The Last Breaking Change | JSON Schema Blog
Truth. Zod is comparable to JSON Schema plus AJV, and it doesn't compare well at all. Your Zod code is all locked inside TypeScript so not only can it not be shared to any other language in your stack but it also cannot be serialized, which introduces many limitations. You also miss out on all the JSON Schema ecosystem tooling. (1, 2) For example the intellisense you get in VS Code for config files is powered by JSON Schema and schemastore.
The very first line of text below the header on the json-schema.org homepage is:
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How to use FastAPI for microservices in Python
The framework's official website mentions a number of pros of FastAPI. In my opinion, the most useful features from a microservice perspective are: the simplicity of code (easy to use and avoid boilerplate), high operational capacity thanks to Starlette and Pydantic and compatibility with industry standards - OpenAPI and JSON Schema.
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How to handle forms in a good way?
I've used Felte to reduce form boilerplate. Felte supports several different validation libraries like Zod. I actually used a custom validation function with ajv (which uses JSON schema).
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A Brief Defense of XML
(There is already a JSON Schema definition at https://json-schema.org/)
Like you said - standard XML isn't terrible. Adding on an XSD isn't terrible, because now you can enforce structure and datatypes on files provided by outside parties. Creating an XSLT is much more of a mental challenge, and probably should be left to tools to define.
Anything beyond those technologies is someone polishing up their resume.
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On the seventh day of Enhancing: Forms
While the aws-sdk is being installed to simulate DynamoDB locally, let me explain a few things about this command. First Comment will be the name of the model the scaffold creates. This model will be codified under app/models/schemas/comment.mjs as a JSON Schema object. Each of the parameters after Comment will be split into a property name and type (e.g. property name “subject”, property type “string”). This JSON Schema document will be used to validate the form data both on the client and server sides.
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Server Sent UI Schema Driven UIs
What you are looking is called Json-schema. Have a look at the implementations page, which will give you an idea of what you can do with json-schema, which also includes UI rendering.
- Tool to document Firestore 'schema'
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?
zod - TypeScript-first schema validation with static type inference
Cerberus - Lightweight, extensible data validation library for Python
ajv - The fastest JSON schema Validator. Supports JSON Schema draft-04/06/07/2019-09/2020-12 and JSON Type Definition (RFC8927)
nexe - 🎉 create a single executable out of your node.js apps
JSON-Schema Faker - JSON-Schema + fake data generators
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
fastify-swagger - Swagger documentation generator for Fastify
SQLAlchemy - The Database Toolkit for Python
Superstruct - A simple and composable way to validate data in JavaScript (and TypeScript).
sqlmodel - SQL databases in Python, designed for simplicity, compatibility, and robustness.
datree - Prevent Kubernetes misconfigurations from reaching production (again 😤 )! From code to cloud, Datree provides an E2E policy enforcement solution to run automatic checks for rule violations. See our docs: https://hub.datree.io
mypy - Optional static typing for Python