disdantic 0.1.0

The missing polymorphic engine for Pydantic. disdantic simplifies registries and discriminated unions with automatic model discovery and auto-importing, helping you manage polymorphic data shapes with less boilerplate.

The missing polymorphic engine for Pydantic.

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Overview

disdantic is a lightweight Python toolkit designed to simplify Pydantic subclass registries, dynamic polymorphic unions, and automatic model discovery. By eliminating the manual boilerplate of maintaining union types and tracking child class imports, it allows you to build clean, extensible, and self-updating polymorphic domain models.

Why Use disdantic?

• Decoupled Registries: Fully isolated subclass tracking namespaces prevent collisions between distinct model domains.
• Dynamic Tagged Unions: Automatic core schema generation dynamically routes incoming JSON payload validation based on a customizable discriminator key.
• Topological Schema Rebuilding: Dynamic subclass registrations trigger cascade schema reloading up the dependent parent MRO trees.
• Automatic Discovery & Auto-Import: Traverses folders recursively to discover and import submodules, ensuring subclasses register themselves without manual imports.
• Robust Object Introspection: Extracts slots, properties, and attributes into sanitized primitives, handling circular references and lazy loader proxies safely.
• CLI Diagnostics Suite: Scans, lists, validates compilation integrity, and exports schemas.

Comparisons

Feature	Pure Pydantic v2	Pydantic + disdantic
Union Type Definitions	Manual list (e.g., Union[A, B, C])	Automatic tagged union via registry base class
New Subclass Adding	Modify parent union type and import	Register via decorator; schema cascades automatically
Dynamic Import Scanning	Manual importlib boilerplate	Declarative packages scan via AutoImporterMixin
Integrity Auditing	Manual script validation	Programmatic and CLI-based diagnostics
Schema Generation	model_json_schema() on static types	Command-line extraction via disdantic schema

Quick Start

Installation

pip install disdantic

For advanced features like YAML serialization, install the optional package extra:

pip install disdantic[yaml]

Core Usage Example

from typing import Literal
from disdantic import PydanticClassRegistryMixin
from pydantic import BaseModel

# 1. Define a polymorphic base registry class
class Message(PydanticClassRegistryMixin):
    schema_discriminator = "msg_type"  # Custom tag field name
    msg_type: str

# 2. Register subclass implementations dynamically
@Message.register("text")
class TextMessage(Message):
    msg_type: Literal["text"] = "text"
    content: str

@Message.register("image")
class ImageMessage(Message):
    msg_type: Literal["image"] = "image"
    url: str
    caption: str | None = None

# 3. Parents automatically rebuild to accommodate new subtypes
class ChatRoom(BaseModel):
    room_name: str
    messages: list[Message]  # Polymorphic union field

# 4. Incoming payloads validate dynamically to correct subclass types
payload = {
    "room_name": "General Chat",
    "messages": [
        {"msg_type": "text", "content": "Hello world!"},
        {"msg_type": "image", "url": "https://placehold.co/150.png", "caption": "Logo"}
    ]
}

room = ChatRoom.model_validate(payload)
assert isinstance(room.messages[0], TextMessage)
assert isinstance(room.messages[1], ImageMessage)

# 5. Full marshalling flow (serialization and deserialization)
room_data = room.model_dump()
# msg_type is automatically included in the serialized output!
assert room_data["messages"][0]["msg_type"] == "text"
assert room_data["messages"][1]["msg_type"] == "image"

restored_room = ChatRoom.model_validate(room_data)
assert isinstance(restored_room.messages[0], TextMessage)
assert isinstance(restored_room.messages[1], ImageMessage)

Component Architecture

• src/disdantic/: Library package containing runtime implementations.
  • registry.py: Core RegistryMixin, PydanticClassRegistryMixin, and global RegistryManager.
  • model.py: Abstract ReloadableBaseModel enabling topological cascading rebuilds.
  • diagnose.py: Registry integrity check orchestrator and compile validation check.
  • introspection.py: Recursively maps complex objects to primitives via InfoMixin.
  • loading.py: Thread-safe deferred instantiation with LazyLoader and LazyProxy.
  • settings.py: Centralized Settings utilizing Pydantic Settings.
• tests/: Multi-tiered testing suite (python/unit/, python/integration/, and e2e/).
• docs/: Markdown files compiled using Zensical static site generator.
• examples/: Self-contained runnable scripts demonstrating configurations.

Advanced Usage & Documentation

For detailed information on configuration settings, custom handlers, CLI commands, and operational guides, visit the Documentation Site.

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines and DEVELOPING.md for development setup instructions.

Ensure you adhere to our Code of Conduct in all community interactions.

Support & Security

• For help and general questions, see SUPPORT.md.
• To report a security vulnerability, please refer to our Security Policy.

License

Licensed under the Apache License 2.0. See the LICENSE file for details.

Citations

If you use this repository or the resulting software in your research, please cite it using the following BibTeX entry:

@software{disdantic,
  author = {markurtz},
  title = {disdantic},
  year = 2026,
  url = {https://github.com/markurtz/disdantic}
}