Welcome to your guide on using O! My Models, a powerful library that allows developers to generate various ORM models from SQL DDL statements. In essence, this library is akin to a magical translator that converts your database schema into Python code, making it easy for you to interact with databases.

Getting Started

To begin, you need to install the library. Let’s simply break that down!

Installation

bash
pip install omymodels

How to Use O! My Models

This library is versatile, and you can generate ORM models in different formats such as SQLAlchemy, Pydantic, GinoORM, and Python Dataclasses. Let’s explore how to create models from DDL statements.

Generating Models from SQL DDL

Imagine you’re a chef, and the DDL statement is a recipe. Each ingredient (table definition) allows you to create a delicious dish (Python model). Here’s how it works:

from omymodels import create_models

ddl = """
CREATE TABLE user_history (
    runid decimal(21) null,
    job_id decimal(21) null,
    id varchar(100) not null,
    user varchar(100) not null,
    status varchar(10) not null,
    event_time timestamp not null default now(),
    comment varchar(1000) not null default none
);
"""

result = create_models(ddl, models_type=pydantic)

In this example, the chef takes his recipe written in DDL and translates it into delicious Pydantic models in a matter of seconds! The resulting model would look like this:

import datetime
from typing import Optional
from pydantic import BaseModel

class UserHistory(BaseModel):
    runid: Optional[int]
    job_id: Optional[int]
    id: str
    user: str
    status: str
    event_time: datetime.datetime
    comment: str

Each ingredient in the recipe has been transformed into a Python attribute, making it ready for you to use in your application.

Using the Command Line

If you prefer to work from a command line, you can call the script directly by using the omm command along with your DDL file:

omm pathtoyour.ddl

You can also define a target path to save the models. The library is highly configurable!

Troubleshooting

While using the O! My Models library, you may encounter some hiccups. Here are a few common issues and their solutions:

• Issue: Unsupported types in DDL.
• Solution: Add your custom type into the types_mapping dictionary before generating models:

from omymodels.models.gino import types
types.types_mapping.update({'your_type_from_ddl': db.TypeInGino})
    

• If you continue to have difficulties, ensure your DDL syntax is correct and matches the expected formats.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.