python-sqlalchemy

@martinffx · 收录于 1 周前

SQLAlchemy ORM patterns for Python database access. Use when defining models, writing queries, implementing upserts, working with JSON columns, or managing database sessions.

场景模型定义查询编写数据库会话管理

适合你，如果常用 Python 开发数据库应用

/ 下载安装

python-sqlalchemy.skill双击,或拖进 Claude 桌面版 / Cowork,即完成安装↓ .skill ↓ .zip

用别的 agent?下载 .zip 解压,把文件夹放进它的技能目录

Claude Code~/.claude/skills/(项目级 .claude/skills/)

Codex CLI~/.codex/skills/

Cursor自动读取上面两处目录

其他工具见其文档的「skills」目录;两个下载是同一份文件,只是名字不同

/ 通过 npx 安装 校验哈希

npx oh-my-skill add martinffx/atelier/python-sqlalchemy

/ 通过 bash 安装

curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- martinffx/atelier/python-sqlalchemy

/ 已经装过?验证本机副本,不用重装

npx oh-my-skill verify martinffx/atelier/python-sqlalchemy

安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。

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怎么用

技能原文 SKILL.md作者撰写 · MIT · e5f96ba

SQLAlchemy ORM Patterns

Modern SQLAlchemy 2.0+ patterns for database access in Python applications.

Model Definition

from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
from sqlalchemy import String
from uuid import UUID
from decimal import Decimal

class Base(DeclarativeBase):
    pass

class ProductModel(Base):
    __tablename__ = "products"

    id: Mapped[UUID] = mapped_column(primary_key=True)
    name: Mapped[str] = mapped_column(String(100))
    price: Mapped[Decimal]
    in_stock: Mapped[bool] = mapped_column(default=True)

Session Management

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine("postgresql://user:pass@localhost/db")
SessionLocal = sessionmaker(bind=engine)

def get_db():
    db = SessionLocal()
    try:
        yield db
    finally:
        db.close()

Query Patterns

# Select
stmt = select(ProductModel).where(ProductModel.price > 100)
products = session.execute(stmt).scalars().all()

# Filter
products = session.query(ProductModel).filter(ProductModel.in_stock == True).all()

# Get by ID
product = session.get(ProductModel, product_id)

# Count
count = session.query(ProductModel).count()

Upsert

from sqlalchemy.dialects.postgresql import insert

stmt = insert(ProductModel).values(
    id=product_id,
    name="Widget",
    price=9.99,
)

# On conflict, update
stmt = stmt.on_conflict_do_update(
    index_elements=["id"],
    set_={"name": stmt.excluded.name, "price": stmt.excluded.price},
)

session.execute(stmt)
session.commit()

Relationships

from sqlalchemy.orm import relationship

class UserModel(Base):
    __tablename__ = "users"
    id: Mapped[int] = mapped_column(primary_key=True)
    orders: Mapped[list["OrderModel"]] = relationship(back_populates="user")

class OrderModel(Base):
    __tablename__ = "orders"
    id: Mapped[int] = mapped_column(primary_key=True)
    user_id: Mapped[int] = mapped_column(ForeignKey("users.id"))
    user: Mapped["UserModel"] = relationship(back_populates="orders")

JSON Columns

from sqlalchemy import JSON

class ConfigModel(Base):
    __tablename__ = "configs"
    id: Mapped[int] = mapped_column(primary_key=True)
    settings: Mapped[dict] = mapped_column(JSON)

# Query JSON field
configs = session.query(ConfigModel).filter(
    ConfigModel.settings["theme"] == "dark"
).all()

See references/ for model patterns, query optimization, and async SQLAlchemy.

按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

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