pydantic-di

A lightweight dependency loading and injection package for Python.

pydantic-di provides a small dependency system for loading objects from environment variables, Python callables, Pydantic models, attrs classes, and custom loaders. It is designed to feel familiar if you have used FastAPI dependencies, while also working outside FastAPI.

Migration from auth-broker

As of pydantic-di version 0.2.2, this package has moved out of the auth-broker organisation, been renamed, and had its import namespace updated.

Item	Previous	Current
GitHub repository	auth-broker/package-dependency	mattcoulter7/pydantic-di
PyPI package	ab-dependency	pydantic-di
Install command	pip install ab-dependency	pip install pydantic-di
Import namespace	ab_core.dependency	pydantic_di

The old PyPI package is retained as an archived historical package. New work should use pydantic-di and pydantic_di.

Features

• Load Pydantic models from environment variables
• Load primitive values from environment variables
• Support discriminated unions
• Support attrs classes by converting them to Pydantic-compatible models
• Support singleton-style persistent dependencies
• Support transient dependencies
• Inject dependencies into:
  • sync functions
  • async functions
  • sync generators
  • async generators
  • classes
  • Pydantic models
• Support generator dependency cleanup
• Support FastAPI dependency integration
• Support flattened environment variable conventions
• Support JSON serialised complex values, such as lists

Installation

pip install pydantic-di

Or with uv:

uv add pydantic-di

Basic usage

from pydantic import BaseModel

from pydantic_di import Load


class AppConfig(BaseModel):
    host: str = "localhost"
    port: int = 8080


config = Load(AppConfig)

print(config.host)
print(config.port)

By default, object models are loaded from environment variables using the model name converted to env-var style.

For AppConfig, the default prefix is:

APP_CONFIG

So these environment variables:

APP_CONFIG_HOST=0.0.0.0
APP_CONFIG_PORT=8000

produce:

AppConfig(host="0.0.0.0", port=8000)

Environment variable naming

Model names are converted from PascalCase or camelCase to uppercase snake case.

OAuth2TokenStore -> O_AUTH2_TOKEN_STORE
HTTPServerConfig -> HTTP_SERVER_CONFIG
AppConfig        -> APP_CONFIG

Field names are appended to the prefix.

APP_CONFIG_HOST=0.0.0.0
APP_CONFIG_PORT=8000

Nested field names are flattened using underscores.

class DatabaseConfig(BaseModel):
    host: str
    port: int


class AppConfig(BaseModel):
    database: DatabaseConfig

APP_CONFIG_DATABASE_HOST=localhost
APP_CONFIG_DATABASE_PORT=5432

Loading primitive values

Use LoaderEnvironment when loading a single primitive value from a specific environment variable.

from pydantic_di.loaders import LoaderEnvironment

port = LoaderEnvironment[int](key="PORT").load()

PORT=8080

The value is validated and cast using Pydantic.

Loader defaults

There are two default mechanisms with different semantics:

• default_value: whole-result fallback when no source value is loaded.
• default_values: object-field defaults for model loaders, merged with loaded values.

Whole-result fallback (default_value)

Use this for scalar or single-value loaders.

from pydantic_di.loaders import LoaderEnvironment

port = LoaderEnvironment[int](
    key="PORT",
    default_value=8080,
).load()

If PORT is missing, this returns 8080.

Falsy defaults are supported, including 0, False, and empty strings.

Partial object defaults (default_values)

Use this with object loaders to provide field-level defaults that are merged with loaded values.

from pydantic import BaseModel
from pydantic_di.loaders import ObjectLoaderEnvironment


class Credentials(BaseModel):
    username: str
    password: str


class ServiceConfig(BaseModel):
    api_key: str
    port: int
    credentials: Credentials


config = ObjectLoaderEnvironment[ServiceConfig](
    env_prefix="MY_CONFIG",
    default_values={
        "api_key": "default",
        "port": 5432,
        "credentials": {
            "username": "admin",
            "password": "secret",
        },
    },
).load()

With:

MY_CONFIG_PORT=6432
MY_CONFIG_CREDENTIALS_USERNAME=matt

The effective data is:

{
    "api_key": "default",  # from default_values
    "port": "6432",  # from env
    "credentials": {
        "username": "matt",  # from env
        "password": "secret",  # from default_values
    },
}

Important notes:

• default_values should be provided in model-field shape.
• Environment values always override defaults for matching leaves.
• Environment key flattening and field alignment are applied to loaded source data.

Persistent dependencies

Load(..., persist=True) caches the loaded dependency.

from pydantic import BaseModel
from pydantic_di import Load


class Client(BaseModel):
    name: str = "client"


one = Load(Client, persist=True)
two = Load(Client, persist=True)

assert one is two

Transient dependencies are created each time.

one = Load(Client, persist=False)
two = Load(Client, persist=False)

assert one is not two
assert one == two

Lazy dependencies

Use Depends to defer loading until call time.

from typing import Annotated

from pydantic import BaseModel

from pydantic_di import Depends, inject


class Settings(BaseModel):
    value: str = "hello"


@inject
def run(settings: Annotated[Settings, Depends(Settings)]):
    return settings.value


assert run() == "hello"

Function injection

from typing import Annotated

from pydantic import BaseModel

from pydantic_di import Depends, inject


class Database(BaseModel):
    url: str = "sqlite://"


@inject
def handler(db: Annotated[Database, Depends(Database)]):
    return db.url

Dependencies are only resolved when the argument was not explicitly provided.

handler(Database(url="postgresql://"))

Async function injection

from typing import Annotated

from pydantic_di import Depends, inject


async def make_token() -> str:
    return "abc"


@inject
async def handler(token: Annotated[str, Depends(make_token)]):
    return token

Generator dependency support

Generator dependencies are entered before the function runs and cleaned up afterwards.

from typing import Annotated

from pydantic_di import Depends, inject


def resource():
    try:
        yield "resource"
    finally:
        print("closed")


@inject
def handler(value: Annotated[str, Depends(resource)]):
    return value

Exceptions are thrown back into the generator so except and finally blocks can run.

def resource():
    try:
        yield "resource"
    except Exception:
        print("caught")
        raise
    finally:
        print("closed")

Class injection

from typing import Annotated

from pydantic import BaseModel

from pydantic_di import Depends, inject


class Settings(BaseModel):
    value: str = "hello"


@inject
class Service:
    settings: Annotated[Settings, Depends(Settings)]

    def run(self):
        return self.settings.value

Pydantic model injection

from typing import Annotated

from pydantic import BaseModel

from pydantic_di import Depends, inject


class Settings(BaseModel):
    value: str = "hello"


@inject
class AppConfig(BaseModel):
    settings: Annotated[Settings, Depends(Settings)]
    retries: int = 3

If a field is supplied by input data, the dependency is not resolved.

FastAPI integration

Depends subclasses FastAPI's dependency parameter when FastAPI is installed.

from typing import Annotated

from fastapi import Depends as FDepends, FastAPI
from pydantic import BaseModel

from pydantic_di import Depends, inject


class SomeDependency(BaseModel):
    value: str = "injected"


def provide_dependency() -> SomeDependency:
    return SomeDependency()


@inject
def context(dep: Annotated[SomeDependency, Depends(provide_dependency)]):
    try:
        yield dep
    finally:
        pass


app = FastAPI()


@app.get("/")
def route(dep: Annotated[SomeDependency, FDepends(context)]):
    return {"value": dep.value}

Discriminated unions

Discriminated unions are supported through Pydantic's Discriminator.

from typing import Annotated, Literal

from pydantic import BaseModel, Discriminator

from pydantic_di import Load


class FileStore(BaseModel):
    type: Literal["FILE"] = "FILE"
    path: str


class S3Store(BaseModel):
    type: Literal["S3"] = "S3"
    bucket: str


Store = Annotated[FileStore | S3Store, Discriminator("type")]

store = Load(Store)

Environment variables:

STORE_TYPE=S3
STORE_S3_BUCKET=my-bucket

Result:

S3Store(type="S3", bucket="my-bucket")

Flattened discriminator convention

For discriminated unions, the discriminator selects which nested branch is used.

DUMMY_STORE_TYPE=A
DUMMY_STORE_A_FOO=hello
DUMMY_STORE_A_NUM=42

This becomes:

{
    "type": "A",
    "foo": "hello",
    "num": 42,
}

attrs support

attrs classes can be loaded by converting them into Pydantic-compatible models.

import attrs

from pydantic_di import Load
from pydantic_di.pydanticize import pydanticize_type


@attrs.define
class Settings:
    host: str = "localhost"
    port: int = 8080


SettingsModel = pydanticize_type(Settings)
settings = Load(SettingsModel)

attrs defaults and factories are preserved.

List support

Simple lists can be supplied as JSON strings.

from pydantic import BaseModel

from pydantic_di import Load


class Config(BaseModel):
    values: list[str]

CONFIG_VALUES='["A", "B", "C"]'

Result:

Config(values=["A", "B", "C"])

Planned recursive list environment convention

For recursive object loading, lists may also be represented as indexed environment variables.

Simple values:

CONFIG_VALUES_0=A
CONFIG_VALUES_1=B
CONFIG_VALUES_2=C

Equivalent JSON form:

CONFIG_VALUES='["A", "B", "C"]'

Lists of Pydantic models:

from typing import Annotated, Literal

from pydantic import BaseModel, Discriminator


class BlahItem(BaseModel):
    type: Literal["blah"] = "blah"
    label: str


class OtherItem(BaseModel):
    type: Literal["other"] = "other"
    label: str


Item = Annotated[BlahItem | OtherItem, Discriminator("type")]


class SomeObject(BaseModel):
    list_field: list[Item]

Environment variables:

SOME_OBJECT_LIST_FIELD_0_TYPE=blah
SOME_OBJECT_LIST_FIELD_0_BLAH_LABEL=first
SOME_OBJECT_LIST_FIELD_1_TYPE=other
SOME_OBJECT_LIST_FIELD_1_OTHER_LABEL=second

Expected result:

SomeObject(
    list_field=[
        BlahItem(type="blah", label="first"),
        OtherItem(type="other", label="second"),
    ]
)

This keeps backwards compatibility with the existing JSON form while allowing recursive, schema-aware environment unpacking.

Custom loaders

Create a custom loader by subclassing LoaderBase.

from typing import Any

from pydantic_di.loaders.base import LoaderBase


class MyLoader(LoaderBase[str]):
    key: str

    def load_raw(self) -> Any:
        return f"value-for-{self.key}"

Then use it directly:

loader = MyLoader[str](key="example")
value = loader.load()

Public API

from pydantic_di import (
    Depends,
    Load,
    inject,
    sentinel,
    pydanticize_data,
    pydanticize_type,
    pydanticize_object,
    cached_type_adapter,
    is_supported_by_pydantic,
)

Design notes

Load resolves immediately.

settings = Load(Settings)

Depends resolves lazily.

settings: Annotated[Settings, Depends(Settings)]

persist=True caches by load target or loaded type.

Depends(Settings, persist=True)

persist=False creates a fresh dependency each time.

Depends(Settings, persist=False)

Development

Run tests:

pytest

Run formatting and linting:

ruff check .
ruff format .

Compatibility goals

The package aims to keep existing behaviour stable:

• Existing JSON list loading should continue to work.
• Existing flat object env-var loading should continue to work.
• Existing discriminator conventions should continue to work.
• New recursive list loading should be additive.