pydapter 0.1.0

Tiny trait + adapter toolkit for pydantic models

pydapter

pydapter is a micro-library that lets any Pydantic model become adaptable to / from arbitrary external representations (JSON, CSV, vector stores, databases …).

from pydapter import Adaptable
from pydapter.adapters import JsonAdapter
from pydantic import BaseModel

class User(Adaptable, BaseModel):
    name: str
    age: int

User.register_adapter(JsonAdapter)

u   = User(name="Alice", age=30)
raw = u.adapt_to(obj_key="json")
u2  = User.adapt_from(raw, obj_key="json")
assert u == u2

The library ships with a tiny core and optional extra adapters you can drop in only when you need them.

Features

• Simple API: Just mix in Adaptable and register adapters
• Extensible: Create your own adapters for any data source
• Type-safe: Leverages Pydantic's validation system
• Async support: Works with async data sources via AsyncAdaptable
• Robust error handling: Comprehensive exception hierarchy for clear error messages

Error Handling

pydapter provides a robust error handling system with a comprehensive exception hierarchy:

try:
    user = User.adapt_from(invalid_data, obj_key="json")
except pydapter.exceptions.ParseError as e:
    print(f"Failed to parse JSON: {e}")
except pydapter.exceptions.ValidationError as e:
    print(f"Validation failed: {e}")
except pydapter.exceptions.AdapterError as e:
    print(f"Other adapter error: {e}")

See Error Handling Documentation for more details.