Support for Pydantic settings configuration file management
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Updated
Apr 28, 2025 - Python
Support for Pydantic settings configuration file management
Use file secrets in nested pydantic-settings models instead of built-in SecretsSettingsSource
Export your Pydantic settings to documentation with ease!
A simple asynchronous API application that implements CRUD user interaction.
Demo for talk about pydantic-settings and best coding practices
Telegram bot based on langraph, providing information about your IT company's services
Referral App
Load pydantic settings from files named by _FILE suffix environment variables
Tutorial for Pydantic and SQLAlchemy
SOPS extension for pydantic-settings
An API built with FastAPI ⚡ that powers the ride-sharing mobile app, Tushare
A lightweight wrapper that catches pydantic-settings validation errors and displays them in a formatted box with field names and error types
Add Consul as source of env variable to settings
'onion_config' is a python package that allows for easy configuration management. It allows for loading and validating configuration data from environment variables and config files in JSON and YAML formats.
This repository is a structured learning guide for Pydantic, covering fundamental concepts, advanced features, validation techniques, settings management, and practical examples to help developers master Pydantic for production-ready Python projects.
Testing LinkML for generating Pydantic data models alongside polyfactory for mock data.
This is a RESTful API for a Social Media Application built using FastAPI and PostgreSQL. It supports core social media features such as user registration, login, post creation, and voting
A User Friendly mobile responsive Website to locate the IP Address
Python Scraper for ArtRadio efficiently gathers real-time song data directly from radio websites, using Selenium for seamless integration and Pydantic for reliable data handling. This scraper updates the playlist database automatically, ensuring up-to-date song tracking for users.
Toy problem for creating an AI inference with AWS-SAM python image with Docker and PyTorch
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