<essential_principles>
SQLAlchemy 2.0 + Pydantic + PostgreSQL Best Practices
This skill provides expert guidance for building production-ready database layers.
Stack
- SQLAlchemy 2.0 with async support (asyncpg driver)
- Pydantic v2 for validation and serialization
- Alembic for migrations
- PostgreSQL only
Core Principles
1. Separation of Concerns
models/ # SQLAlchemy ORM models (database layer)
schemas/ # Pydantic schemas (API layer)
repositories/ # Data access patterns
services/ # Business logic2. Type Safety First Always use SQLAlchemy 2.0 style with Mapped[] type annotations:
from sqlalchemy.orm import Mapped, mapped_column
class User(Base):
__tablename__ = "users"
id: Mapped[int] = mapped_column(primary_key=True)
name: Mapped[str] = mapped_column(String(100))3. Async by Default Use async engine and sessions for FastAPI:
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
engine = create_async_engine("postgresql+asyncpg://...")4. Pydantic-SQLAlchemy Bridge Keep models and schemas separate but mappable:
# Schema reads from ORM
class UserRead(BaseModel):
model_config = ConfigDict(from_attributes=True)5. Repository Pattern Abstract database operations for testability and clean code. </essential_principles>
- Setup database layer - Initialize SQLAlchemy + Pydantic + Alembic from scratch
- Define models - Create SQLAlchemy models with Pydantic schemas
- Create migration - Generate and manage Alembic migrations
- Query patterns - Async CRUD, joins, eager loading, optimization
- Full implementation - Complete database layer for a feature
Auto-detection triggers (use this skill when user mentions):
- database, db, sqlalchemy, postgres, postgresql
- model, migration, alembic
- repository, crud, query
- async session, connection pool
<reference_index>
Domain Knowledge
| Reference | Purpose |
|---|---|
| references/best-practices.md | Production patterns, security, performance |
| references/patterns.md | Repository, Unit of Work, common queries |
| references/async-patterns.md | Async session management, FastAPI integration |
| </reference_index> |
<workflows_index>
| Workflow | Purpose |
|---|---|
| workflows/setup-database.md | Initialize complete database layer |
| workflows/define-models.md | Create models + schemas + relationships |
| workflows/create-migration.md | Alembic migration workflow |
| workflows/query-patterns.md | CRUD operations and optimization |
| </workflows_index> |
<quick_reference>
File Structure
src/
├── db/
│ ├── __init__.py
│ ├── base.py # DeclarativeBase
│ ├── session.py # Engine + async session factory
│ └── dependencies.py # FastAPI dependency
├── models/
│ ├── __init__.py
│ └── user.py # SQLAlchemy models
├── schemas/
│ ├── __init__.py
│ └── user.py # Pydantic schemas
├── repositories/
│ ├── __init__.py
│ ├── base.py # Generic repository
│ └── user.py # User repository
└── alembic/
├── alembic.ini
├── env.py
└── versions/Essential Imports
# Models
from sqlalchemy import String, Integer, ForeignKey, DateTime
from sqlalchemy.orm import Mapped, mapped_column, relationship, DeclarativeBase
# Async
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
# Pydantic
from pydantic import BaseModel, ConfigDict, FieldConnection String
# PostgreSQL async
DATABASE_URL = "postgresql+asyncpg://user:pass@localhost:5432/dbname"</quick_reference>
<success_criteria> Database layer is complete when:
- Async engine and session factory configured
- Base model with common fields (id, created_at, updated_at)
- Models use Mapped[] type annotations
- Pydantic schemas with from_attributes=True
- Alembic configured for async
- Repository pattern implemented
- FastAPI dependency for session injection
- Connection pooling configured for production </success_criteria>