pydantic

Validate and coerce Python data using Pydantic models.

7|Updated Apr 9, 2024

Search Tags:#pydantic #fastapi #type-hints #validate #serialization #data-model #validation-core

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npx skills add https://github.com/figueroaignacio/ai --skill pydantic-figueroaignacio

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pydantic
Source: https://github.com/figueroaignacio/ai/tree/main/.agents/skills/pydantic
Command: npx skills add https://github.com/figueroaignacio/ai --skill pydantic-figueroaignacio

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Pydantic provides robust, typed data validation and coercion for Python applications by defining models that validate input data at runtime, ensuring correctness and consistency.

Core Features & Use Cases

Type-safe models with automatic validation and coercion
Seamless integration with FastAPI, Django, SQLAlchemy, and configuration management
Flexible validators, serialization, and JSON schema generation for API and data workflows

Quick Start

Define a BaseModel with typed fields and validate input with model_validate, then serialize with model_dump.

Frequently Asked Questions about pydantic

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I validate API request data using Python type hints?▼

You can validate API request data by defining a BaseModel with typed fields and calling model_validate on incoming data. Pydantic enforces type safety by automatically validating and coercing inputs at runtime to ensure correctness across APIs.

Can I use Pydantic models for data serialization in FastAPI and Django?▼

Yes, Pydantic models work seamlessly with FastAPI, Django, and SQLAlchemy for data serialization. After validating input data, you can use the model_dump method to serialize the validated models into a standard Python dictionary format.

What is the best way to parse and enforce types in application configuration files?▼

The best way to parse application configuration is by using Pydantic models to enforce type safety. This approach validates and coerces configuration data at runtime, ensuring that parsed settings strictly match your defined type hints before application startup.

Does Pydantic support custom field validators and JSON schema generation?▼

Yes, Pydantic supports flexible field and model validators alongside automatic JSON schema generation. These features allow you to enforce custom validation rules and export standardized schemas for API documentation and data workflows.

How does Pydantic handle high-performance data validation for large pipelines?▼

Pydantic handles high-performance data validation by relying on a Rust-based core called pydantic-core. This architecture allows it to quickly validate and coerce large volumes of data across demanding data pipelines and API workflows.

When do I need ConfigDict migrations in Pydantic?▼

You need ConfigDict migrations when updating models to manage configuration changes during validation. It allows you to define model-level settings that dictate how validation, coercion, and serialization behave across your data pipelines.