| Name | pydantic-filters JSON |
| Version |
0.3.5
JSON |
| download |
| home_page | None |
| Summary | A declarative and intuitive way to describe data filtering and sorting in your application. |
| upload_time | 2025-02-16 18:59:04 |
| maintainer | None |
| docs_url | None |
| author | Nureev Insaf |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
pydantic
sqlalchemy
fastapi
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
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|
# pydantic-filters
[](https://github.com/so-saf/pydantic-filters/actions/workflows/test.yaml)
[](https://codecov.io/gh/so-saf/pydantic-filters)
[](https://pypi.python.org/pypi/pydantic-filters)
[](https://github.com/so-saf/pydantic-filters/blob/main/LICENSE)
[](https://github.com/so-saf/pydantic-filters)
**Documentation:** https://so-saf.github.io/pydantic-filters/
**Source Code:** https://github.com/so-saf/pydantic-filters
---
Describe the filters, not implement them!
A declarative and intuitive way to describe data filtering and sorting in your application.
# Features
- Filtering by the models themselves as well as by related.
- Built-in pagination and sorting.
- Lots of settings and possible customizations.
- The only required dependency is Pydantic.
You can use the basic features without being attached to specific frameworks,
or use one of the supported plugins and drivers:
- Plugins:
- FastAPI >= 0.100.0
- Drivers:
- SQLAlchemy >= 2
# Installation
```shell
pip install pydantic-filters
```
# A Simple Example
`BaseFilter` is just a pydantic model, it should be treated similarly
Let's imagine you have a simple user service with the following SQLAlchemy model:
```python
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
class Base(DeclarativeBase):
pass
class User(Base):
__tablename__ = "users"
id: Mapped[int] = mapped_column(primary_key=True)
name: Mapped[str]
age: Mapped[int]
```
Describe how you would like to filter users using BaseFilter.
```python
from typing import List
from pydantic_filters import BaseFilter
class UserFilter(BaseFilter):
id: List[int]
name: List[str]
name__ilike: str
age__lt: int
age__gt: int
```
`BaseFilter` is just a pydantic model, it should be treated similarly
Next, you need to apply a filter to some query:
```python
from sqlalchemy import select
from pydantic_filters.drivers.sqlalchemy import append_filter_to_statement
statement = select(User)
filter_ = UserFilter(name__ilike="kate", age__lt=23)
stmt = append_filter_to_statement(
statement=statement, model=User, filter_=filter_,
)
```
And get something like:
```sql
SELECT users.id, users.name, users.age
FROM users
WHERE users.name ILIKE 'kate' AND users.age < 23
```
The filter can be used in conjunction with one of the supported web frameworks:
```python
from typing import Annotated
from fastapi import FastAPI, APIRouter
from pydantic_filters.plugins.fastapi import FilterDepends
router = APIRouter()
@router.get("/")
async def get_multiple(
filter_: Annotated[UserFilter, FilterDepends(UserFilter)],
):
...
app = FastAPI(title="User Service")
app.include_router(router, prefix="/users", tags=["User"])
```
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"description": "\n# pydantic-filters\n\n[](https://github.com/so-saf/pydantic-filters/actions/workflows/test.yaml)\n[](https://codecov.io/gh/so-saf/pydantic-filters)\n[](https://pypi.python.org/pypi/pydantic-filters)\n[](https://github.com/so-saf/pydantic-filters/blob/main/LICENSE)\n[](https://github.com/so-saf/pydantic-filters)\n\n**Documentation:** https://so-saf.github.io/pydantic-filters/\n\n**Source Code:** https://github.com/so-saf/pydantic-filters\n\n---\n\nDescribe the filters, not implement them! \nA declarative and intuitive way to describe data filtering and sorting in your application.\n\n# Features\n\n- Filtering by the models themselves as well as by related.\n- Built-in pagination and sorting.\n- Lots of settings and possible customizations.\n- The only required dependency is Pydantic.\nYou can use the basic features without being attached to specific frameworks, \nor use one of the supported plugins and drivers: \n - Plugins:\n - FastAPI >= 0.100.0\n - Drivers: \n - SQLAlchemy >= 2\n\n# Installation\n\n```shell\npip install pydantic-filters\n```\n\n# A Simple Example\n\n`BaseFilter` is just a pydantic model, it should be treated similarly\n\nLet's imagine you have a simple user service with the following SQLAlchemy model:\n\n\n```python\nfrom sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column\n\n\nclass Base(DeclarativeBase):\n pass\n\n\nclass User(Base):\n __tablename__ = \"users\"\n id: Mapped[int] = mapped_column(primary_key=True)\n name: Mapped[str]\n age: Mapped[int]\n```\n\nDescribe how you would like to filter users using BaseFilter.\n\n```python\nfrom typing import List\nfrom pydantic_filters import BaseFilter\n\n\nclass UserFilter(BaseFilter):\n id: List[int]\n name: List[str]\n name__ilike: str\n age__lt: int\n age__gt: int\n```\n\n`BaseFilter` is just a pydantic model, it should be treated similarly\n\nNext, you need to apply a filter to some query:\n\n```python\nfrom sqlalchemy import select\nfrom pydantic_filters.drivers.sqlalchemy import append_filter_to_statement\n\nstatement = select(User)\nfilter_ = UserFilter(name__ilike=\"kate\", age__lt=23)\n\nstmt = append_filter_to_statement(\n statement=statement, model=User, filter_=filter_,\n)\n```\n\nAnd get something like:\n\n```sql\nSELECT users.id, users.name, users.age \nFROM users \nWHERE users.name ILIKE 'kate' AND users.age < 23\n```\n\nThe filter can be used in conjunction with one of the supported web frameworks:\n\n```python\nfrom typing import Annotated\nfrom fastapi import FastAPI, APIRouter\nfrom pydantic_filters.plugins.fastapi import FilterDepends\n\n\nrouter = APIRouter()\n\n\n@router.get(\"/\")\nasync def get_multiple(\n filter_: Annotated[UserFilter, FilterDepends(UserFilter)],\n):\n ...\n\n\napp = FastAPI(title=\"User Service\")\napp.include_router(router, prefix=\"/users\", tags=[\"User\"])\n```\n\n\n\n",
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