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asynchronous ORM that uses pydantic models to represent database tables

Project description


Asynchronous ORM that uses pydantic models to represent database tables ✨

Test Package version Supported Python versions

Ormdantic is a library for interacting with Asynchronous SQL databases from Python code, with Python objects. It is designed to be intuitive, easy to use, compatible, and robust.

Ormdantic is based on Pypika, and powered by Pydantic and SQLAlchemy, and Highly inspired by Sqlmodel, Created by @tiangolo.

What is Pypika?

PyPika is a Python API for building SQL queries. The motivation behind PyPika is to provide a simple interface for building SQL queries without limiting the flexibility of handwritten SQL. Designed with data analysis in mind, PyPika leverages the builder design pattern to construct queries to avoid messy string formatting and concatenation. It is also easily extended to take full advantage of specific features of SQL database vendors.

The key features are:

  • Easy to use: It has sensible defaults and does a lot of work underneath to simplify the code you write.
  • Compatible: It combines SQLAlchemy, Pydantic and Pypika tries to simplify the code you write as much as possible, allowing you to reduce the code duplication to a minimum, but while getting the best developer experience possible.
  • Extensible: You have all the power of SQLAlchemy and Pypika underneath.
  • Short Queries: You can write queries in a single line of code, and it will be converted to the appropriate syntax for the database you are using.


A recent and currently supported version of Python (right now, Python supports versions 3.10 and above).

As Ormdantic is based on Pydantic and SQLAlchemy and Pypika, it requires them. They will be automatically installed when you install Ormdantic.


You can add Ormdantic in a few easy steps. First of all, install the dependency:

$ pip install ormdantic

---> 100%

Successfully installed Ormdantic
  • Install The specific Asynchronous ORM library for your database.
# PostgreSQL
$ pip install ormdantic[postgres]

# SQLite
$ pip install ormdantic[sqlite]


To understand SQL, Sebastian the Creator of FastAPI and SQLModel created an amazing documentation that could help you understand the basics of SQL, ex. CREATE TABLE, INSERT, SELECT, UPDATE, DELETE, etc.

Check out the documentation.

But let's see how to use Ormdantic.

Create SQLAlchemy engine

Ormdantic uses SQLAlchemy under hood to run different queries, which is why we need to initialize by creating an asynchronous engine.

Note: You will use the connection parameter to pass the connection to the engine directly.

from ormdantic import Ormdantic

connection = "sqlite+aiosqlite:///db.sqlite3"

database = Ormdantic(connection)

Note: You can use any asynchronous engine, check out the documentation for more information.

Create a table

To create tables decorate a pydantic model with the database.table decorator, passing the database information ex. Primary key, foreign keys, Indexes, back_references, unique_constraints etc. to the decorator call.

Table Restrictions

  • Tables must have a single column primary key.
  • The primary key column must be the first column.
  • Relationships must union-type the foreign model and that models primary key.
from uuid import uuid4
from pydantic import BaseModel, Field

@database.table(pk="id", indexed=["name"])
class Flavor(BaseModel):
     """A coffee flavor."""

     id: UUID = Field(default_factory=uuid4)
     name: str = Field(max_length=63)


Now after we create the table, we can initialize the database with the table and then run different queries.


  • Register models as ORM models and initialize the database.

We use database.init will Populate relations information and create the tables.

async def demo() -> None:
    async def _init() -> None:
        async with db._engine.begin() as conn:
            await db.init()
            await conn.run_sync(db._metadata.drop_all)
            await conn.run_sync(db._metadata.create_all)
    await _init()


Now let's imagine we have another table called Coffee that has a foreign key to Flavor.

class Coffee(BaseModel):
     """Drink it in the morning."""

     id: UUID = Field(default_factory=uuid4)
     sweetener: str | None = Field(max_length=63)
     sweetener_count: int | None = None
     flavor: Flavor | UUID

After we create the table, we can insert data into the table, using the database.insert method, is away we insert a Model Instance.

# Create a Flavor called "Vanilla"
vanilla = Flavor(name="Vanilla")

# Insert the Flavor into the database
await database[Flavor].insert(vanilla)

# Create a Coffee with the Vanilla Flavor
coffee = Coffee(sweetener="Sugar", sweetener_count=1, flavor=vanilla)

# Insert the Coffee into the database
await database[Coffee].insert(coffee)

Searching Queries

As we know, in SQL, we can search for data using different methods, ex. WHERE, LIKE, IN, BETWEEN, etc.

In Ormdantic, we can search for data using the database.find_one or database.find_many methods.

  • Find_one used to find a Model instance by Primary Key, its could also find with depth parameter.
     # Find one
     vanilla = await database[Flavor].find_one(

     # Find one with depth.
     find_coffee = await database[Coffee].find_one(, depth=1)
  • Find_many used to find Model instances by some condition ex. where, order_by, order, limit, offset, depth.
     # Find many
     await database[Flavor].find_many()

     # Get paginated results.
     await database[Flavor].find_many(
          where={"name": "vanilla"}, order_by=["id", "name"], limit=2, offset=2

Update / Upsert Queries


The modification of data that is already in the database is referred to as updating. You can update individual rows, all the rows in a table, or a subset of all rows. Each column can be updated separately; the other columns are not affected.

     # Update a Flavor = "caramel"
     await database[Flavor].update(flavor)

The Upsert method is similar to the Synchronize method with one exception; the Upsert method does not delete any records. The Upsert method will result in insert or update operations. If the record exists, it will be updated. If the record does not exist, it will be inserted.

     # Upsert a Flavor = "mocha"
     await database[Flavor].upsert(flavor)


The DELETE statement is used to delete existing records in a table.

     # Delete a Flavor
     await database[Flavor].delete(


To count the number of rows of a table or in a result set you can use the count function.

     # Count
     count = await database[Flavor].count()
  • It's support also Where and Depth
     count_advanced = await database[Coffee].count(
          where={"sweetener": 2}, depth=1

Generator Feature

We introduce a new feature called Generator, which is a way to generate a Model instance with random data.

So, Given a Pydantic model type can generate instances of that model with randomly generated values.

using ormdantic.generator.Generator to generate a Model instance.

from enum import auto, Enum
from uuid import UUID

from ormdantic.generator import Generator
from pydantic import BaseModel

class Flavor(Enum):
    MOCHA = auto()
    VANILLA = auto()

class Brand(BaseModel):
    brand_name: str

class Coffee(BaseModel):
    id: UUID
    description: str
    cream: bool
    sweetener: int
    flavor: Flavor
    brand: Brand


so the results will be:

description='ctWOb' cream=True sweetener=234
flavor=<Flavor.VANILLA: 2> brand=Brand(brand_name='LMrIf')

We can integrate this with our database while testing our application (Live Tests).

Development 🚧

Setup environment 📦

You should create a virtual environment and activate it:

python -m venv venv/
source venv/bin/activate

And then install the development dependencies:

# Install dependencies
pip install -r requirements/all.txt

Run tests 🌝

You can run all the tests with:

bash scripts/

Format the code 🍂

Execute the following command to apply pre-commit formatting:

bash scripts/

Execute the following command to apply mypy type checking:

bash scripts/


This project is licensed under the terms of the MIT license.

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