schemaorg
pydantic
schemaorg | pydantic | |
---|---|---|
50 | 167 | |
5,237 | 18,942 | |
0.6% | 3.8% | |
8.3 | 9.8 | |
5 days ago | 6 days ago | |
HTML | Python | |
Apache License 2.0 | 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.
schemaorg
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Make your resume SEO friendly using JSON Resume with microdata
itemscope itemtype="https://schema.org/Person"> itemprop="name">Scott Nath Work History itemprop="alumniOf" itemscope itemtype="https://schema.org/Organization"> itemprop="name">Company ABC itemprop="description">...company description... itemprop="employee" itemscope itemtype="https://schema.org/EmployeeRole"> itemprop="roleName">Software developer itemprop="description">...details about role... itemprop="alumniOf" itemscope itemtype="https://schema.org/Organization"> itemprop="name">Company Meow itemprop="employee" itemscope itemtype="https://schema.org/EmployeeRole"> itemprop="roleName">Sitting Volunteer History itemprop="alumniOf" itemscope itemtype="https://schema.org/Organization"> itemprop="name">Company 501c3 itemprop="employee" itemscope itemtype="https://schema.org/EmployeeRole"> itemprop="roleName">Software developer for free itemprop="description">...details about role...
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How to Boost SEO by Enhancing HTML with Microdata
I've been re-writing the HTML of my site and added structured data, in the form of microdata attributes, following the Schema.org vocabulary set. Structured data can be understood by search engines and other machines, giving your content structure and context.
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The Future of Documentation is Personalized
Implementing Structured Data Markup annotation system, which can provide additional context about the content to search engines. Structured data markup such as Schema.org can be used to aid search engines to understand the content relevance and significance. This approach can help improve search results by improved ranking and visibility of the content.
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Next.js App Router SEO overview
export default async function Page({ params }) { const product = await getProduct(params.id); const jsonLd = { "@context": "https://schema.org", "@type": "Product", name: product.name, image: product.image, description: product.description, }; return ( {/* Add JSON-LD to your page */} {/* ... */} section> ); }
- Melhores Práticas de SEO com Next.js
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How to Add JSON-LD Structured Data to a Next.js Website
const Article = () => { // Dummy article data const article = { title: 'Sample Article Title', description: 'This is a sample article description.', datePublished: '2024-03-23', author: { "@type": "Person", "name": "John Doe" }, image: "https://via.placeholder.com/800x400", publisher: { "@type": "Organization", "name": "Sample News", "logo": { "@type": "ImageObject", "url": "https://via.placeholder.com/200x100" } }, mainEntityOfPage: { "@type": "WebPage", "@id": "https://www.example.com/article" } }; // Define the JsonLd component within the Article component const JsonLd = ({ data }) => ( ); return ( <div> <h1>{article.title}h1> <p>{article.description}p> <p>Date Published: {article.datePublished}p> <p>Author: {article.author.name}p> <img src={article.image} alt={article.title} /> {/* JSON-LD for Article */} <JsonLd data={{ "@context": "https://schema.org", "@type": "NewsArticle", "headline": article.title, "description": article.description, "datePublished": article.datePublished, "author": article.author, "image": [article.image], "publisher": article.publisher, "mainEntityOfPage": article.mainEntityOfPage }} /> div> ); }; export default Article;
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Adding Star Ratings to Google Search Results
itemscope itemtype="http://schema.org/Product"> itemprop="name">Product Name itemprop="description">Product Description itemprop="review" itemscope itemtype="http://schema.org/Review"> itemprop="reviewRating" itemscope itemtype="http://schema.org/Rating"> itemprop="ratingValue">5 stars itemprop="author" itemscope itemtype="http://schema.org/Person"> itemprop="name">Author Name itemprop="datePublished">Date of Review itemprop="reviewBody">Review Body
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Next.js SEO: The Complete Checklist to Boost Your Site Ranking
You can use Schema.org to generate JSON-LD Schema for your website.
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What are some web dev practices you can think of that were pushed so hard at conferences and books but never made it to the real world?
It’s also very much still a thing rebranded as Microdata. You can find examples on Schema.org, but now there are easier ways to share the same info; I prefer JSON-LD.
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How to create a blog with Next.js and React Bricks
Schema.org provides a shared vocabulary that webmasters can use to mark up their pages in ways that can be understood by major search engines, including Google, Bing, Yahoo!, and Yandex.
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?
schema-org-java - Java library for working with Schema.org data in JSON-LD format
Cerberus - Lightweight, extensible data validation library for Python
rupeetravel - Vietnam travel guide for Indians
nexe - 🎉 create a single executable out of your node.js apps
IdentityModel - .NET standard helper library for claims-based identity, OAuth 2.0 and OpenID Connect.
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
decentralized-to-do-list - a decentralized to do list built with web5 sdk
SQLAlchemy - The Database Toolkit for Python
mathesar - Web application providing an intuitive user experience to databases.
sqlmodel - SQL databases in Python, designed for simplicity, compatibility, and robustness.
pydantic_schemaorg - Schema.org classes in pydantic
mypy - Optional static typing for Python