PydanticRDF Documentation

Welcome to the PydanticRDF documentation! This library bridges Pydantic V2 models and RDF graphs, allowing seamless serialization and deserialization between them.

Table of Contents

• Quick Start
• API Reference

Overview

PydanticRDF enables you to define your data models with Pydantic's powerful validation while working with semantic web technologies using rdflib. It makes it easy to:

• Map Pydantic models to RDF types
• Serialize model instances to RDF graphs
• Deserialize RDF data into typed, validated Pydantic models
• Handle nested models, circular references, and complex types

The library is designed to be simple and intuitive, following Pydantic's design principles.

Key Features

• Type Safety: Full type checking and validation powered by Pydantic
• Simple API: Intuitive interfaces for serialization and deserialization
• Seamless Integration: Works with existing Pydantic and RDF workflows
• Flexible Mapping: Custom predicate and datatype annotations
• Comprehensive Support: Handles nested models, lists, and circular references
• JSON Schema: Generate valid JSON schemas with proper URI field handling

Example

from rdflib import SDO
from pydantic_rdf import BaseRdfModel, WithPredicate
from typing import Annotated

# Define a model using Schema.org types
class Person(BaseRdfModel):
    rdf_type = SDO.Person
    _rdf_namespace = SDO

    name: str
    email: str
    job_title: Annotated[str, WithPredicate(SDO.jobTitle)]

# Create an instance
person = Person(
    uri=SDO.Person_1,
    name="John Doe",
    email="john.doe@example.com",
    job_title="Software Engineer"
)

# Serialize to RDF
graph = person.model_dump_rdf()

# Deserialize from RDF
loaded_person = Person.parse_graph(graph, SDO.Person_1)

For more detailed examples and guides, check out the rest of the documentation!