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2025-08-17 22:18:45 +08:00
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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# Virtual environments
.venv/
venv/
ENV/
env/
.python-version
# PyTest
.pytest_cache/
# IDE
.idea/
.vscode/
# macOS
.DS_Store
# uv
.uv/

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# Changelog
All notable changes to this project will be documented in this file.
## 0.1.0 - Initial release
- Project scaffold with src layout
- Sync/async engines and sessions
- Models (Hero, Team) and CRUD helpers
- Optional CLI commands
- Tests and conda recipe

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MIT License
Copyright (c) 2025
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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.PHONY: help db-up db-down db-logs py-install-all test test-sqlite test-pg test-pg-once examples
PY_VERSIONS ?= 3.10 3.11 3.12 3.13
help:
@echo "Targets:"
@echo " db-up - start Postgres (docker compose)"
@echo " db-down - stop Postgres (docker compose)"
@echo " db-logs - tail DB logs"
@echo " test - run all tests (incl. smoke)"
@echo " test-sqlite - run fast smoke tests on SQLite"
@echo " test-pg - run PG integration once on current Python"
@echo " test-pg-once - run PG integration across Python 3.10–3.13"
@echo " py-install-all - install Python versions with uv"
@echo " examples - run example scripts (requires SQL_* env)"
db-up:
docker compose up -d
db-down:
docker compose down -v
db-logs:
docker compose logs -f
# Fast local tests. Runs whatever pytest finds (no network/install).
test:
pytest -ra
# Only the SQLite-backed smoke test (fast, no Postgres required).
test-sqlite:
pytest -ra tests/test_smoke.py
# Single-run Postgres integration on the active interpreter.
test-pg:
@[ -n "$$SQL_HOST" ] || [ -n "$$PGHOST" ] || (echo "Set SQL_HOST/PGHOST (and SQL_PORT/PGPORT, SQL_USER/PGUSER, SQL_PASSWORD/PGPASSWORD, SQL_DATABASE)" && exit 1)
@[ -n "$$SQL_PORT" ] || [ -n "$$PGPORT" ] || (echo "Set SQL_PORT/PGPORT" && exit 1)
@[ -n "$$SQL_USER" ] || [ -n "$$PGUSER" ] || (echo "Set SQL_USER/PGUSER" && exit 1)
@[ -n "$$SQL_PASSWORD" ] || [ -n "$$PGPASSWORD" ] || (echo "Set SQL_PASSWORD/PGPASSWORD" && exit 1)
@[ -n "$$SQL_DATABASE" ] || [ -n "$$PGDATABASE" ] || (echo "Set SQL_DATABASE/PGDATABASE" && exit 1)
pytest -ra -m integration tests/test_pg_integration.py
test-pg-once:
@[ -n "$$SQL_HOST" ] || [ -n "$$PGHOST" ] || (echo "Set SQL_HOST/PGHOST (and SQL_PORT/PGPORT, SQL_USER/PGUSER, SQL_PASSWORD/PGPASSWORD, SQL_DATABASE)" && exit 1)
@[ -n "$$SQL_PORT" ] || [ -n "$$PGPORT" ] || (echo "Set SQL_PORT/PGPORT" && exit 1)
@[ -n "$$SQL_USER" ] || [ -n "$$PGUSER" ] || (echo "Set SQL_USER/PGUSER" && exit 1)
@[ -n "$$SQL_PASSWORD" ] || [ -n "$$PGPASSWORD" ] || (echo "Set SQL_PASSWORD/PGPASSWORD" && exit 1)
@[ -n "$$SQL_DATABASE" ] || [ -n "$$PGDATABASE" ] || (echo "Set SQL_DATABASE/PGDATABASE" && exit 1)
uv python install $(PY_VERSIONS)
@for py in $(PY_VERSIONS); do \
echo "==> PG CRUD test on $$py"; \
UV_VENV_CLEAR=1 uv venv -p $$py .venv-$$py; \
. .venv-$$py/bin/activate; \
uv pip install -e . pytest; \
pytest -ra -m integration tests/test_pg_integration.py || exit $$?; \
deactivate; \
done
py-install-all:
uv python install $(PY_VERSIONS)
# Run examples against a configured Postgres.
# Requires env vars: SQL_HOST/PGHOST, SQL_PORT/PGPORT, SQL_USER/PGUSER, SQL_PASSWORD/PGPASSWORD, SQL_DATABASE/PGDATABASE
examples:
@[ -n "$$SQL_HOST" ] || [ -n "$$PGHOST" ] || (echo "Set SQL_HOST/PGHOST (and SQL_PORT/PGPORT, SQL_USER/PGUSER, SQL_PASSWORD/PGPASSWORD, SQL_DATABASE)" && exit 1)
@[ -n "$$SQL_PORT" ] || [ -n "$$PGPORT" ] || (echo "Set SQL_PORT/PGPORT" && exit 1)
@[ -n "$$SQL_USER" ] || [ -n "$$PGUSER" ] || (echo "Set SQL_USER/PGUSER" && exit 1)
@[ -n "$$SQL_PASSWORD" ] || [ -n "$$PGPASSWORD" ] || (echo "Set SQL_PASSWORD/PGPASSWORD" && exit 1)
@[ -n "$$SQL_DATABASE" ] || [ -n "$$PGDATABASE" ] || (echo "Set SQL_DATABASE/PGDATABASE" && exit 1)
uv python install 3.12
UV_VENV_CLEAR=1 uv venv -p 3.12 .venv-examples
. .venv-examples/bin/activate; \
uv pip install -e .; \
set -e; \
echo "==> Running examples/01_sync_crud.py"; \
python examples/01_sync_crud.py; \
echo "==> Running examples/02_bulk_and_filters.py"; \
python examples/02_bulk_and_filters.py; \
echo "==> Running examples/03_relationships.py"; \
python examples/03_relationships.py; \
echo "==> Running examples/04_async_crud.py"; \
python examples/04_async_crud.py; \
echo "==> Running examples/05_cheminformatics.py"; \
python examples/05_cheminformatics.py; \
deactivate

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# sqlmodel-pg-kit
Reusable SQLModel + PostgreSQL kit with src layout, sync/async engines, and generic CRUD repositories. Managed via uv (PEP 621/517) and built with hatchling. Includes minimal tests and a conda recipe.
## Features
- Config dataclass -> builds sync/async URLs
- Engine + Session factories (sync/async)
- Generic `Repository`/`AsyncRepository` with create/get/list/update/delete/bulk_insert
- Examples and notebooks for common SQLModel patterns
- PyPI/conda packaging setup, smoke test
## Quickstart (uv)
- Create venv: `uv venv`
- Editable install: `uv pip install -e .`
- Run tests: `uv pip install pytest && pytest -q`
## Usage
- Configure environment (either `SQL_*` or Postgres `PG*` vars). Example for container ADDR:
- `export SQL_HOST=192.168.64.8`
- `export SQL_PORT=5432`
- `export SQL_USER=postgres`
- `export SQL_PASSWORD=change-me-strong`
- `export SQL_DATABASE=appdb`
- `export SQL_SSLMODE=disable`
You can perform CRUD with SQLModel without writing raw SQL strings. This kit exposes:
- `create_all()` to create tables from models collected in `SQLModel.metadata`
- Generic repositories: `Repository(Model)` and `AsyncRepository(Model)` for CRUD and bulk insert
- Session helpers: `get_session()` and `get_async_session()` for custom queries using SQLModel/SQLAlchemy expressions
### Choose Backend: SQLite or Postgres
- SQLite (in-memory) quick demo — no environment variables needed:
```python
from typing import Optional
from sqlmodel import SQLModel, Field
from sqlmodel_pg_kit import create_all
from sqlmodel_pg_kit import db # access engine factory
# Override engine to SQLite in-memory
db.engine = db.create_engine("sqlite:///:memory:", echo=False)
class Hero(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
name: str
create_all() # creates tables on the current engine (SQLite)
```
- SQLite (file) quick demo:
```python
from sqlmodel_pg_kit import db, create_all
db.engine = db.create_engine("sqlite:///./demo.db", echo=False)
create_all()
```
- Postgres (recommended for production) via environment variables:
```bash
export SQL_HOST=127.0.0.1
export SQL_PORT=5432
export SQL_USER=postgres
export SQL_PASSWORD=change-me-strong
export SQL_DATABASE=appdb
export SQL_SSLMODE=disable # or require/verify-full as needed
```
Then your Python code can just call:
```python
from sqlmodel_pg_kit import create_all
create_all() # uses the default Postgres engine constructed from env
```
- Postgres explicit configuration (no env needed):
```python
from sqlmodel_pg_kit import db, create_all
cfg = db.DatabaseConfig(
host="127.0.0.1", port=5432, user="postgres",
password="change-me-strong", database="appdb", sslmode="disable",
)
db.engine = db.create_engine(cfg.sync_url(), echo=False)
create_all()
```
### CSV → SQLModel → Table (Auto‑generate Model)
Auto‑generate a SQLModel class from a CSV header and import the rows.
- CLI example:
```bash
uv run python examples/06_csv_to_sqlmodel.py --csv ./data/molecules.csv --sqlite ./demo.db
# Or with Postgres via env vars:
# export SQL_HOST=...; export SQL_USER=...; ...
# uv run python examples/06_csv_to_sqlmodel.py --csv ./data/molecules.csv
```
- In code:
```python
from sqlmodel_pg_kit.csv_import import build_model_from_csv, insert_rows
from sqlmodel_pg_kit import create_all
from sqlmodel_pg_kit.db import get_session
spec, rows = build_model_from_csv("./data/molecules.csv", class_name="Molecules", table_name="molecules")
create_all()
with get_session() as s:
n = insert_rows(spec.model, rows, s)
print("inserted:", n)
```
Type inference rules per column: bool if values are true/false/1/0/yes/no; else int; else float; otherwise str. Empty/NA/NaN/None/null become NULL.
### Sync CRUD (helpers)
```bash
uv run python examples/01_sync_crud.py
```
Shows create/get/list/update/delete using Repository. Minimal snippet:
```python
from typing import Optional
from sqlmodel import SQLModel, Field
from sqlmodel_pg_kit import create_all, Repository
from sqlmodel_pg_kit.db import get_session
class Hero(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
name: str = Field(index=True)
age: Optional[int] = None
create_all()
repo = Repository(Hero)
with get_session() as s:
h = repo.create(s, {"name": "Alice", "age": 20})
h2 = repo.get(s, h.id)
page = repo.list(s, page=1, size=5)
h3 = repo.update(s, h.id, age=21)
ok = repo.delete(s, h.id)
```
### Bulk insert + filters/pagination
```bash
uv run python examples/02_bulk_and_filters.py
```
Demonstrates `Repository.bulk_insert(rows)` and filtering with SQLModel expressions:
```python
from typing import Optional
from sqlmodel import select, SQLModel, Field
from sqlmodel_pg_kit import Repository
from sqlmodel_pg_kit.db import get_session
class Hero(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
name: str
age: Optional[int] = None
with get_session() as s:
Repository(Hero).bulk_insert(s, [{"name":"PG Hero","age":1},{"name":"PG Hero","age":2}])
heroes = s.exec(select(Hero).where(Hero.name == "PG Hero")).all()
```
### Relationships (Team <- Hero)
```bash
uv run python examples/03_relationships.py
```
Creates a `Team`, assigns heroes, and reads back with `selectinload` eager loading.
### Async CRUD
```bash
uv run python examples/04_async_crud.py
```
Uses `get_async_session()` with `AsyncRepository` to write/read without raw SQL. You may call `create_all()` once before async operations.
## Tests via Makefile
- `make test-sqlite`: run fast smoke tests on SQLite (no Postgres needed)
- `make test`: run all tests found by pytest
- `make test-pg`: run Postgres integration once on current Python (requires SQL_*/PG* env)
- `make test-pg-once`: run Postgres integration across Python 3.10–3.13 (requires `uv` and SQL_*/PG* env)
## Cheminformatics Example
This repo includes a practical multi-table example tailored for cheminformatics workflows: `examples/05_cheminformatics.py`.
What it shows:
- Molecules with descriptors: `smiles`, `selfies`, `qed`, `sa_score` (no RDKit dependency in runtime; compute upstream and store).
- Many-to-many datasets: `Dataset` and link table `MoleculeDataset` for train/holdout/etc.
- Dataclass interop: lightweight `@dataclass MoleculeDTO` that converts to SQLModel for fast CRUD.
- Typical queries: threshold filters, pattern matching, eager-loaded relationships, join filters.
Run it (requires SQL_*/PG* env):
```bash
uv run python examples/05_cheminformatics.py
```
Step-by-step outline you can adapt:
1) Define models
```python
class Molecule(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
smiles: str = Field(index=True)
selfies: Optional[str] = None
qed: Optional[float] = Field(default=None, index=True)
sa_score: Optional[float] = Field(default=None, index=True)
datasets: List[Dataset] = Relationship(back_populates="molecules", link_model=MoleculeDataset)
class Dataset(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
name: str = Field(index=True)
molecules: List[Molecule] = Relationship(back_populates="datasets", link_model=MoleculeDataset)
class MoleculeDataset(SQLModel, table=True):
molecule_id: int = Field(foreign_key="molecule.id", primary_key=True)
dataset_id: int = Field(foreign_key="dataset.id", primary_key=True)
```
2) Bring your RDKit pipeline data via dataclass
```python
@dataclass
class MoleculeDTO:
smiles: str
selfies: Optional[str] = None
qed: Optional[float] = None
sa_score: Optional[float] = None
def to_model(self) -> Molecule:
return Molecule(**vars(self))
```
3) Fast CRUD
```python
from sqlmodel_pg_kit.db import get_session
create_all() # once
with get_session() as s:
s.add(Molecule(smiles="CCO", qed=0.5, sa_score=2.1))
s.commit()
with get_session() as s:
m = s.exec(select(Molecule).where(Molecule.smiles == "CCO")).one()
m.qed = 0.55
s.add(m); s.commit(); s.refresh(m)
with get_session() as s:
s.delete(m); s.commit()
```
4) Link datasets and join queries
```python
with get_session() as s:
ds = Dataset(name="train"); s.add(ds); s.commit(); s.refresh(ds)
m = s.exec(select(Molecule).where(Molecule.smiles == "CCO")).one()
s.add(MoleculeDataset(molecule_id=m.id, dataset_id=ds.id)); s.commit()
with get_session() as s:
stmt = (
select(Molecule)
.join(MoleculeDataset, Molecule.id == MoleculeDataset.molecule_id)
.join(Dataset, Dataset.id == MoleculeDataset.dataset_id)
.where(Dataset.name == "train")
)
train_mols = s.exec(stmt).all()
```
5) Tips for high-throughput tasks
- Bulk insert many rows: use SQLAlchemy Core `insert(Model).values(list_of_dicts)` then `s.commit()`.
- Paginate: `select(Model).offset((page-1)*size).limit(size)`.
- Eager-load related rows: `.options(selectinload(Model.rel))` to avoid N+1.
- Partial updates: load row, set fields, commit; or use Core `update()` when you don’t need ORM instances.
See `examples/05_cheminformatics.py` for a complete, runnable walkthrough.
## Cheminformatics: RDKit + Mordred
Below are patterns to integrate RDKit and Mordred descriptor pipelines.
- Modeling patterns:
- Wide columns: put high‑value fields directly on `Molecule` (e.g., `qed`, `sa_score`) with B‑Tree indexes for fast filters.
- JSONB payload: store a large descriptor dict in a JSONB column when you need many descriptors but query only a subset.
- Normalized table (EAV): `MoleculeDescriptor(molecule_id, name, value)` when you frequently query and index specific descriptors by name.
- JSONB example (Postgres):
```python
from typing import Optional, Dict
from sqlmodel import SQLModel, Field
from sqlalchemy import Column
from sqlalchemy.dialects.postgresql import JSONB
class Molecule(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
smiles: str = Field(index=True)
descriptors: Dict[str, float] = Field(default_factory=dict, sa_column=Column(JSONB))
```
- EAV example:
```python
class MoleculeDescriptor(SQLModel, table=True):
molecule_id: int = Field(foreign_key="molecule.id", index=True)
name: str = Field(index=True)
value: float = Field(index=True)
```
- RDKit + Mordred pipeline sketch:
```python
# Optional installs in Jupyter:
# %pip install rdkit-pypi mordred
from rdkit import Chem
from rdkit.Chem import QED
from mordred import Calculator, descriptors
mol = Chem.MolFromSmiles("c1ccccc1O")
qed = float(QED.qed(mol))
calc = Calculator(descriptors, ignore_3D=True)
md = calc(mol) # returns mapping-like object
desc = {k: float(v) for k,v in md.items() if v is not None and isinstance(v, (int, float))}
from sqlmodel_pg_kit.db import get_session
from sqlmodel import select
with get_session() as s:
m = Molecule(smiles="c1ccccc1O", descriptors=desc)
s.add(m); s.commit(); s.refresh(m)
# filter by a descriptor threshold
# for JSONB: use SQLAlchemy JSON operators or extract into wide columns for hot features
```
Indexing tips:
- Create B‑Tree indexes on hot numeric columns: `qed`, `sa_score`, etc.
- For JSONB, consider GIN indexes with jsonb_path_ops on frequently accessed keys.
- For EAV, add `(name, value)` composite indexes and partial indexes for the top N descriptor names.
## Jupyter Notebooks
A ready‑to‑run tutorial notebook is included under `notebooks/`:
- `notebooks/01_cheminformatics_quickstart.ipynb`: end‑to‑end walkthrough (install, configuration, CRUD, joins, optional RDKit/Mordred computation). You can execute it in your Jupyter environment.
- `notebooks/05_cheminformatics.ipynb`: a teaching version of `examples/05_cheminformatics.py`, step‑by‑step CRUD and joins tailored for learning.
- `notebooks/01_sync_crud.ipynb`: helpers-based create/get/list/update/delete (with optional SQLite override).
- `notebooks/02_bulk_and_filters.ipynb`: bulk insert and SQLModel filtering examples.
- `notebooks/03_relationships.ipynb`: Team ↔ Hero relationships with eager loading.
- `notebooks/04_async_crud.ipynb`: async session CRUD, with optional async SQLite override.
- `notebooks/06_csv_import.ipynb`: CSV → SQLModel 自动建模与入库（含 SQLite/PG 切换与筛选查询）。
Typical start in Jupyter:
```python
%pip install -e . pytest # kit + tests
# Optional dependencies for chem pipelines:
# %pip install rdkit-pypi mordred
```
### Micromamba environment
If you already have a micromamba env named `sqlmodel`:
```bash
micromamba activate sqlmodel
jupyter lab # or jupyter notebook
```
Then open one of the notebooks under `notebooks/` to follow along.
## Build & Publish
- PyPI build: `uv build`
- PyPI publish: `uv publish` (configure token via `uv keyring set ...`)
- Conda build: `conda build conda/`
## Notes
- For production, prefer `sslmode=verify-full`, least-privileged DB users, and consider PgBouncer.
- Bring your own Alembic migrations; set `target_metadata = SQLModel.metadata` in env.py.
## apple container start command
```shell
# 停止并删除现有容器
container stop pg-app
container delete pg-app
container run \
--name pg-app \
--detach \
--mount type=tmpfs,target=/var/lib/postgresql/data \
--volume ./docker-init:/docker-entrypoint-initdb.d \
--env POSTGRES_USER=postgres \
--env POSTGRES_PASSWORD=change-me-strong \
--env POSTGRES_INITDB_ARGS="--encoding=UTF8 --locale=en_US.UTF-8" \
postgres:16
container ls --all
container logs pg-app
```
## set environment variables
```bash
export SQL_HOST=192.168.64.8
export SQL_PORT=5432
export SQL_USER=postgres
export SQL_PASSWORD=change-me-strong
export SQL_DATABASE=appdb
export SQL_SSLMODE=disable
```
## make test with uv
测试连接
现在您可以使用psql测试PostgreSQL连接：
```bash
psql -h $SQL_HOST -p $SQL_PORT -U $SQL_USER -d $SQL_DATABASE -c '\l'
```
```bash
make test-pg-once
```
## Verify data with psql
- List tables: `\dt`
- Inspect example tables (depending on the example you ran), e.g.:
- `SELECT * FROM hero ORDER BY id DESC LIMIT 10;`
- `SELECT * FROM dataset ORDER BY id DESC LIMIT 10;`

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{% set name = "sqlmodel-pg-kit" %}
{% set version = "0.1.0" %}
package:
name: {{ name|lower }}
version: {{ version }}
source:
path: ..
build:
noarch: python
script: "{{ PYTHON }} -m pip install . -vv"
requirements:
host:
- python >=3.10
- pip
run:
- python >=3.10
- sqlmodel >=0.0.16
- psycopg >=3.1
- asyncpg >=0.29
- alembic >=1.13
- pydantic >=2.6
about:
license: MIT
summary: Reusable SQLModel + PostgreSQL kit.

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smiles,selfies,qed,sa_score,active,count,weight
CCO,,0.45,2.1,true,10,46.07
c1ccccc1,,0.76,3.5,false,5,78.11
CCN(CC)CC,,0.62,2.8,true,8,101.19
1 smiles selfies qed sa_score active count weight
2 CCO 0.45 2.1 true 10 46.07
3 c1ccccc1 0.76 3.5 false 5 78.11
4 CCN(CC)CC 0.62 2.8 true 8 101.19

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services:
pg-app:
image: postgres:16
container_name: pg-app
restart: always
ports:
- "127.0.0.1:5433:5432" # bind to localhost only
environment:
POSTGRES_USER: postgres
POSTGRES_PASSWORD: change-me-strong
POSTGRES_INITDB_ARGS: "--encoding=UTF8 --locale=en_US.UTF-8"
volumes:
- ./data:/var/lib/postgresql/data
- ./docker-init:/docker-entrypoint-initdb.d
healthcheck:
test: ["CMD-SHELL", "pg_isready -U $$POSTGRES_USER -d postgres"]
interval: 10s
timeout: 5s
retries: 5

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-- 1) 如果没有就创建应用用户
DO $$
BEGIN
IF NOT EXISTS (SELECT 1 FROM pg_roles WHERE rolname = 'appuser') THEN
CREATE ROLE appuser LOGIN PASSWORD 'changeme';
END IF;
END
$$;
-- 2) 如果没有就创建数据库（顶层语句，不能放在 DO/事务里）
SELECT
'CREATE DATABASE appdb ENCODING ''UTF8'' TEMPLATE template0 ' ||
'LC_COLLATE ''en_US.UTF-8'' LC_CTYPE ''en_US.UTF-8'';'
WHERE NOT EXISTS (SELECT 1 FROM pg_database WHERE datname = 'appdb')
\gexec
-- 3) 设定库所有者/权限（无论是否新建都可安全执行）
ALTER DATABASE appdb OWNER TO appuser;
GRANT ALL PRIVILEGES ON DATABASE appdb TO appuser;
-- 4) 进入 appdb，配置 schema 与默认权限（可选但常用）
\connect appdb
GRANT ALL ON SCHEMA public TO appuser;
ALTER SCHEMA public OWNER TO appuser;
-- 让以后在 public 里新建的对象默认给 appuser 权限（可按需保留）
ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT ALL ON TABLES TO appuser;
ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT ALL ON SEQUENCES TO appuser;

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"""
Sync CRUD examples using SQLModel and the kit's generic Repository.
Prereq:
- Export SQL_*/PG* env vars to point at your Postgres
- Run this file: uv run python examples/01_sync_crud.py
"""
from typing import Optional, List
from sqlmodel import SQLModel, Field
from sqlmodel_pg_kit import create_all, Repository
from sqlmodel_pg_kit.db import get_session
class Hero(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
name: str = Field(index=True)
age: Optional[int] = None
def main():
# Ensure tables exist
create_all()
repo = Repository(Hero)
with get_session() as s:
# Create
h = repo.create(s, {"name": "Alice", "age": 20})
print("Created:", h)
# Read by id
h2 = repo.get(s, h.id)
print("Fetched by id:", h2)
# Update
h3 = repo.update(s, h.id, age=21)
print("Updated:", h3)
# List (pagination)
page = repo.list(s, page=1, size=5)
print("List page=1,size=5 ->", [x.name for x in page])
# Delete
ok = repo.delete(s, h.id)
print("Deleted?", ok)
if __name__ == "__main__":
main()

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"""
Bulk insert and filtering with the generic Repository and SQLModel sessions.
Run: uv run python examples/02_bulk_and_filters.py
"""
from typing import List, Optional
from sqlmodel import select, SQLModel, Field
from sqlmodel_pg_kit import create_all, Repository
from sqlmodel_pg_kit.db import get_session
class Hero(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
name: str = Field(index=True)
age: Optional[int] = None
def main():
create_all()
repo = Repository(Hero)
# Clean slate (optional) – delete all heroes
with get_session() as s:
s.exec(select(Hero)) # warm up
s.execute(Hero.__table__.delete())
s.commit()
rows = [
{"name": "PG Hero", "age": 1},
{"name": "PG Hero", "age": 2},
{"name": "Bob", "age": 30},
{"name": "Carol", "age": 40},
]
n = repo.bulk_insert(s, rows)
print(f"Bulk inserted: {n}")
# Filter by name using SQLModel/SQLAlchemy expressions
with get_session() as s:
heroes: List[Hero] = s.exec(select(Hero).where(Hero.name == "PG Hero")).all()
print("Filter name='PG Hero' ->", [(h.id, h.age) for h in heroes])
# Range filtering and ordering
with get_session() as s:
res = s.exec(select(Hero).where(Hero.age >= 2).order_by(Hero.age.asc())).all()
print("Age >= 2 asc ->", [(h.name, h.age) for h in res])
if __name__ == "__main__":
main()

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"""
Demonstrate relationships and joined reads using SQLModel with generic kit.
Run: uv run python examples/03_relationships.py
"""
from typing import List, Optional
from sqlalchemy.orm import selectinload
from sqlmodel import SQLModel, select, Field, Relationship
from sqlmodel_pg_kit import create_all
from sqlmodel_pg_kit.db import get_session
class Team(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
name: str = Field(index=True)
heroes: List["Hero"] = Relationship(back_populates="team")
class Hero(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
name: str = Field(index=True)
age: Optional[int] = None
team_id: Optional[int] = Field(default=None, foreign_key="team.id")
team: Optional[Team] = Relationship(back_populates="heroes")
def main():
create_all()
with get_session() as s:
# Clean
s.execute(Hero.__table__.delete())
s.execute(Team.__table__.delete())
s.commit()
# Create Team and Heroes
t = Team(name="Avengers")
s.add(t)
s.commit()
s.refresh(t)
h1 = Hero(name="Thor", age=1500, team_id=t.id)
h2 = Hero(name="Hulk", age=49, team_id=t.id)
s.add(h1)
s.add(h2)
s.commit()
# Query heroes and eager-load team via selectinload
stmt = select(Hero).options(selectinload(Hero.team)).order_by(Hero.id.asc())
heroes: List[Hero] = s.exec(stmt).all()
print([(h.name, h.team.name if h.team else None) for h in heroes])
if __name__ == "__main__":
main()

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"""
Async example using the provided async engine/session and generic AsyncRepository.
Run: uv run python examples/04_async_crud.py
"""
import asyncio
from typing import Optional
from sqlmodel import select, SQLModel, Field
from sqlmodel_pg_kit import create_all, AsyncRepository
from sqlmodel_pg_kit.db import get_async_session
class Hero(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
name: str = Field(index=True)
age: Optional[int] = None
async def amain():
# Ensure tables exist (sync helper is fine to call once)
create_all()
repo = AsyncRepository(Hero)
# Clean and insert using async session
async with get_async_session() as s:
await s.execute(Hero.__table__.delete())
await s.commit()
await repo.create(s, {"name": "Async Hero", "age": 7})
res = await s.execute(select(Hero))
heroes = res.scalars().all()
print([h.name for h in heroes])
if __name__ == "__main__":
asyncio.run(amain())

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"""
Cheminformatics example: multi-table schema, dataclass interop, CRUD, joins.
Prereq:
- Export SQL_*/PG* env vars to point at your Postgres
- Run: uv run python examples/05_cheminformatics.py
This example does NOT require RDKit; it stores fields you can compute
externally (smiles, selfies, qed, sa_score). If RDKit is available, you
can compute those before inserting.
"""
from __future__ import annotations
from dataclasses import dataclass
from datetime import datetime
from typing import List, Optional
from sqlalchemy.orm import selectinload
from sqlmodel import SQLModel, Field, Relationship, select
from sqlmodel_pg_kit.db import get_session
from sqlmodel_pg_kit import create_all as _create_all # reuse engine + metadata
# --- Models
class MoleculeDataset(SQLModel, table=True):
molecule_id: int = Field(foreign_key="molecule.id", primary_key=True)
dataset_id: int = Field(foreign_key="dataset.id", primary_key=True)
added_at: datetime = Field(default_factory=datetime.utcnow)
class Molecule(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
smiles: str = Field(index=True)
selfies: Optional[str] = Field(default=None)
qed: Optional[float] = Field(default=None, index=True)
sa_score: Optional[float] = Field(default=None, index=True)
created_at: datetime = Field(default_factory=datetime.utcnow)
updated_at: datetime = Field(default_factory=datetime.utcnow)
datasets: List["Dataset"] = Relationship(back_populates="molecules", link_model=MoleculeDataset)
class Dataset(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
name: str = Field(index=True)
molecules: List["Molecule"] = Relationship(back_populates="datasets", link_model=MoleculeDataset)
# --- Dataclass DTO (handy for RDKit pipelines)
@dataclass
class MoleculeDTO:
smiles: str
selfies: Optional[str] = None
qed: Optional[float] = None
sa_score: Optional[float] = None
def to_model(self) -> Molecule:
return Molecule(
smiles=self.smiles,
selfies=self.selfies,
qed=self.qed,
sa_score=self.sa_score,
)
def create_all():
# Ensure all tables in this example (plus base kit) exist
_create_all()
def main():
create_all()
# Clean existing data for a repeatable run
with get_session() as s:
s.execute(MoleculeDataset.__table__.delete())
s.execute(Molecule.__table__.delete())
s.execute(Dataset.__table__.delete())
s.commit()
# Create molecules from dataclass (as you would after RDKit computation)
mols = [
MoleculeDTO(smiles="CCO", selfies=None, qed=0.45, sa_score=2.1),
MoleculeDTO(smiles="c1ccccc1", selfies=None, qed=0.76, sa_score=3.5),
MoleculeDTO(smiles="CCN(CC)CC", selfies=None, qed=0.62, sa_score=2.8),
]
with get_session() as s:
for dto in mols:
s.add(dto.to_model())
s.commit()
# Create datasets and link molecules (many-to-many)
with get_session() as s:
ds_train = Dataset(name="train")
ds_holdout = Dataset(name="holdout")
s.add(ds_train)
s.add(ds_holdout)
s.commit()
s.refresh(ds_train)
s.refresh(ds_holdout)
# Link: first two in train, last one in holdout
mol_list: List[Molecule] = s.exec(select(Molecule).order_by(Molecule.id.asc())).all()
links = [
MoleculeDataset(molecule_id=mol_list[0].id, dataset_id=ds_train.id),
MoleculeDataset(molecule_id=mol_list[1].id, dataset_id=ds_train.id),
MoleculeDataset(molecule_id=mol_list[2].id, dataset_id=ds_holdout.id),
]
s.add_all(links)
s.commit()
# CRUD: update a descriptor (e.g., refined QED)
with get_session() as s:
mol = s.exec(select(Molecule).where(Molecule.smiles == "CCO")).one()
mol.qed = 0.50
mol.updated_at = datetime.utcnow()
s.add(mol)
s.commit()
s.refresh(mol)
print("Updated CCO ->", mol.qed)
# Filtering: typical queries
with get_session() as s:
# QED threshold and order by SA score
hi_qed = s.exec(
select(Molecule).where(Molecule.qed >= 0.6).order_by(Molecule.sa_score.asc())
).all()
print("qed>=0.6 order by sa_score:", [(m.smiles, m.qed, m.sa_score) for m in hi_qed])
# Pattern search on SMILES (prefix demo; production use proper search)
starts_with_cc = s.exec(select(Molecule).where(Molecule.smiles.like("CC%"))).all()
print("SMILES like 'CC%':", [m.smiles for m in starts_with_cc])
# Joins: list molecules with dataset name (eager-load relationships)
with get_session() as s:
stmt = (
select(Molecule)
.options(selectinload(Molecule.datasets))
.order_by(Molecule.id.asc())
)
molecules = s.exec(stmt).all()
print("with datasets:", [(m.smiles, [d.name for d in m.datasets]) for m in molecules])
# Join filter: only molecules in 'train'
with get_session() as s:
stmt = (
select(Molecule)
.join(MoleculeDataset, Molecule.id == MoleculeDataset.molecule_id)
.join(Dataset, Dataset.id == MoleculeDataset.dataset_id)
.where(Dataset.name == "train")
.order_by(Molecule.id.asc())
)
train_mols = s.exec(stmt).all()
print("in train:", [m.smiles for m in train_mols])
# Delete: drop a molecule
with get_session() as s:
target = s.exec(select(Molecule).where(Molecule.smiles == "CCN(CC)CC")).one()
s.delete(target)
s.commit()
left = s.exec(select(Molecule).order_by(Molecule.id.asc())).all()
print("after delete:", [m.smiles for m in left])
if __name__ == "__main__":
main()

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"""
Import a CSV into the database by auto-generating a SQLModel class.
Usage:
uv run python examples/06_csv_to_sqlmodel.py --csv path/to/file.csv \
[--class-name MyTable] [--table-name my_table] \
[--sqlite-memory | --sqlite FILE]
If neither --sqlite-* is given, the script uses Postgres via SQL_* / PG* env vars.
"""
from __future__ import annotations
import argparse
from typing import Optional
from sqlmodel_pg_kit import create_all
from sqlmodel_pg_kit.csv_import import build_model_from_csv, insert_rows, create_indexes
from sqlmodel_pg_kit import db
from sqlmodel_pg_kit.db import get_session
def parse_args() -> argparse.Namespace:
ap = argparse.ArgumentParser("csv-to-sqlmodel")
ap.add_argument("--csv", required=True, help="Path to CSV file")
ap.add_argument("--class-name", help="Override generated SQLModel class name (default: CSV stem)")
ap.add_argument("--table-name", help="Override table name (default: snake_case of class name)")
g = ap.add_mutually_exclusive_group()
g.add_argument("--sqlite-memory", action="store_true", help="Use in-memory SQLite")
g.add_argument("--sqlite", help="Use SQLite file path (e.g., ./demo.db)")
ap.add_argument(
"--null",
action="append",
default=[],
help="Add custom null sentinel (repeatable). Default includes '',na,nan,none,null",
)
ap.add_argument(
"--type",
action="append",
default=[],
help="Type override mapping NAME=TYPE (TYPE in bool,int,float,str). Repeatable.",
)
ap.add_argument(
"--rename",
action="append",
default=[],
help="Rename mapping OLD=NEW for columns before sanitization. Repeatable.",
)
ap.add_argument(
"--index",
action="append",
default=[],
help="Create B-Tree index on column (repeatable).",
)
return ap.parse_args()
def maybe_override_engine(args: argparse.Namespace) -> None:
if args.sqlite_memory:
db.engine = db.create_engine("sqlite:///:memory:", echo=False)
elif args.sqlite:
db.engine = db.create_engine(f"sqlite:///{args.sqlite}", echo=False)
def main() -> None:
args = parse_args()
maybe_override_engine(args)
# Parse mappings
def parse_kv(items):
m = {}
for it in items:
if "=" not in it:
raise SystemExit(f"Invalid mapping '{it}', expected NAME=VALUE")
k, v = it.split("=", 1)
m[k.strip()] = v.strip()
return m
rename_map = parse_kv(args.rename)
type_map_raw = parse_kv(args.type)
type_map = {}
for k, v in type_map_raw.items():
v_lower = v.lower()
if v_lower == "bool":
type_map[k] = bool
elif v_lower == "int":
type_map[k] = int
elif v_lower == "float":
type_map[k] = float
elif v_lower == "str":
type_map[k] = str
else:
raise SystemExit(f"Unsupported type '{v}' for column '{k}' (use bool,int,float,str)")
spec, rows = build_model_from_csv(
args.csv,
class_name=args.class_name,
table_name=args.table_name,
null_values=args.null or None,
type_overrides=type_map or None,
rename_map=rename_map or None,
warn_on_nulls=True,
)
# Create table for the generated model
create_all()
# Insert rows
with get_session() as s:
n = insert_rows(spec.model, rows, s)
print(f"Created table '{spec.table_name}' and inserted {n} rows.")
# Create indexes if requested
if args.index:
created = create_indexes(spec.model, args.index, db.engine)
print("Created indexes:", created)
if __name__ == "__main__":
main()

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notebooks/01_cheminformatics_quickstart.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# sqlmodel-pg-kit — Cheminformatics Quickstart\n\n",
"This notebook demonstrates how to use `sqlmodel-pg-kit` for day-to-day data work, including:\n",
"- Environment setup (SQLite smoke vs PostgreSQL)\n",
"- Core CRUD with SQLModel sessions\n",
"- Multi-table schema and joins for molecules and datasets\n",
"- Optional RDKit + Mordred descriptor computation and storage\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 0. Install (in Jupyter)\n",
"Uncomment as needed to install the package and optional chem deps."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# %pip install -e . pytest\n",
"# Optional: RDKit + Mordred\n",
"# %pip install rdkit-pypi mordred\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Basic kit usage (SQLite smoke)\n",
"Use the built-in smoke test pattern with an in-memory SQLite DB to verify CRUD paths."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"\n",
"# Add the src directory to the path so we can import sqlmodel_pg_kit\n",
"sys.path.insert(0, os.path.join(os.getcwd(), '..', 'src'))\n",
"\n",
"from sqlmodel_pg_kit import db, create_all, Repository\n",
"\n",
"# Override to SQLite in-memory for quick check\n",
"db.cfg = db.DatabaseConfig(host='', port=0, user='', password='', database=':memory:', sslmode='disable')\n",
"db.engine = db.create_engine('sqlite:///:memory:', echo=False)\n",
"\n",
"from typing import Optional\n",
"from sqlmodel import SQLModel, Field\n",
"\n",
"class Hero(SQLModel, table=True):\n",
" id: Optional[int] = Field(default=None, primary_key=True)\n",
" name: str = Field(index=True)\n",
" age: Optional[int] = None\n",
"\n",
"create_all()\n",
"repo = Repository(Hero)\n",
"from sqlmodel_pg_kit.db import get_session\n",
"with get_session() as s:\n",
" repo.create(s, {'name': 'Iron Man', 'age': 45})\n",
" [h.name for h in repo.list(s)]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Connect to PostgreSQL (optional)\n",
"Export `SQL_*` or `PG*` variables in your shell before starting Jupyter or set them here."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"# Example (adjust to your environment, uncomment to set in-process):\n",
"# os.environ['SQL_HOST'] = '127.0.0.1'\n",
"# os.environ['SQL_PORT'] = '5432'\n",
"# os.environ['SQL_USER'] = 'postgres'\n",
"# os.environ['SQL_PASSWORD'] = 'change-me-strong'\n",
"# os.environ['SQL_DATABASE'] = 'appdb'\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Cheminformatics schema (Molecule, Dataset, MoleculeDataset)\n",
"Define models inline (you can also place them in your own package)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import annotations\n",
"from dataclasses import dataclass\n",
"from datetime import datetime\n",
"from typing import List, Optional\n",
"\n",
"from sqlmodel_pg_kit.db import get_session, engine\n",
"from sqlmodel import SQLModel, Field, Relationship, select\n",
"from sqlalchemy.orm import Mapped\n",
"\n",
"class MoleculeDataset(SQLModel, table=True):\n",
" molecule_id: int = Field(foreign_key='molecule.id', primary_key=True)\n",
" dataset_id: int = Field(foreign_key='dataset.id', primary_key=True)\n",
" added_at: datetime = Field(default_factory=datetime.utcnow)\n",
"\n",
"class Molecule(SQLModel, table=True):\n",
" id: Optional[int] = Field(default=None, primary_key=True)\n",
" smiles: str = Field(index=True)\n",
" selfies: Optional[str] = Field(default=None)\n",
" qed: Optional[float] = Field(default=None, index=True)\n",
" sa_score: Optional[float] = Field(default=None, index=True)\n",
" created_at: datetime = Field(default_factory=datetime.utcnow)\n",
" updated_at: datetime = Field(default_factory=datetime.utcnow)\n",
" datasets: Mapped[List[\"Dataset\"]] = Relationship(back_populates=\"molecules\", link_model=MoleculeDataset)\n",
"\n",
"class Dataset(SQLModel, table=True):\n",
" id: Optional[int] = Field(default=None, primary_key=True)\n",
" name: str = Field(index=True)\n",
" molecules: Mapped[List[\"Molecule\"]] = Relationship(back_populates=\"datasets\", link_model=MoleculeDataset)\n",
"\n",
"@dataclass\n",
"class MoleculeDTO:\n",
" smiles: str\n",
" selfies: Optional[str] = None\n",
" qed: Optional[float] = None\n",
" sa_score: Optional[float] = None\n",
" def to_model(self) -> Molecule:\n",
" return Molecule(**vars(self))\n",
"\n",
"# Create tables for these models\n",
"SQLModel.metadata.create_all(engine)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. CRUD and common queries\n",
"Insert molecules/datasets, link them, filter and join."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Clean\n",
"with get_session() as s:\n",
" s.execute(MoleculeDataset.__table__.delete())\n",
" s.execute(Molecule.__table__.delete())\n",
" s.execute(Dataset.__table__.delete())\n",
" s.commit()\n",
"\n",
"# Insert molecules via DTO\n",
"mols = [\n",
" MoleculeDTO(smiles='CCO', qed=0.45, sa_score=2.1),\n",
" MoleculeDTO(smiles='c1ccccc1', qed=0.76, sa_score=3.5),\n",
" MoleculeDTO(smiles='CCN(CC)CC', qed=0.62, sa_score=2.8),\n",
"]\n",
"with get_session() as s:\n",
" s.add_all([dto.to_model() for dto in mols])\n",
" s.commit()\n",
"\n",
"# Datasets and linking\n",
"with get_session() as s:\n",
" ds_train = Dataset(name='train'); ds_holdout = Dataset(name='holdout')\n",
" s.add_all([ds_train, ds_holdout]); s.commit(); s.refresh(ds_train); s.refresh(ds_holdout)\n",
" mol_list = s.exec(select(Molecule).order_by(Molecule.id.asc())).all()\n",
" links = [\n",
" MoleculeDataset(molecule_id=mol_list[0].id, dataset_id=ds_train.id),\n",
" MoleculeDataset(molecule_id=mol_list[1].id, dataset_id=ds_train.id),\n",
" MoleculeDataset(molecule_id=mol_list[2].id, dataset_id=ds_holdout.id),\n",
" ]\n",
" s.add_all(links); s.commit()\n",
"\n",
"# Update one\n",
"with get_session() as s:\n",
" mol = s.exec(select(Molecule).where(Molecule.smiles=='CCO')).one()\n",
" mol.qed = 0.50; s.add(mol); s.commit(); s.refresh(mol)\n",
" mol.qed\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Filters and joins\n",
"from sqlalchemy.orm import selectinload\n",
"\n",
"with get_session() as s:\n",
" hi_qed = s.exec(select(Molecule).where(Molecule.qed>=0.6).order_by(Molecule.sa_score.asc())).all()\n",
" hi_qed_view = [(m.smiles, m.qed, m.sa_score) for m in hi_qed]\n",
"\n",
"with get_session() as s:\n",
" stmt = select(Molecule).options(selectinload(Molecule.datasets)).order_by(Molecule.id.asc())\n",
" mols_with_ds = s.exec(stmt).all()\n",
" mols_with_ds_view = [(m.smiles, [d.name for d in m.datasets]) for m in mols_with_ds]\n",
"\n",
"with get_session() as s:\n",
" stmt = (select(Molecule)\n",
" .join(MoleculeDataset, Molecule.id==MoleculeDataset.molecule_id)\n",
" .join(Dataset, Dataset.id==MoleculeDataset.dataset_id)\n",
" .where(Dataset.name=='train')\n",
" .order_by(Molecule.id.asc()))\n",
" train_mols = s.exec(stmt).all()\n",
" train_mols_view = [m.smiles for m in train_mols]\n",
"\n",
"hi_qed_view, mols_with_ds_view, train_mols_view\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Optional: RDKit + Mordred integration\n",
"Compute descriptors and store them. If you prefer flexible storage, use a JSONB column or a normalized EAV table."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" from rdkit import Chem\n",
" from rdkit.Chem import QED\n",
" from mordred import Calculator, descriptors\n",
" rdkit_ok = True\n",
"except Exception as e:\n",
" rdkit_ok = False\n",
" print('RDKit/Mordred not available in this environment. Skipping demo.\n', e)\n",
"\n",
"if rdkit_ok:\n",
" mol = Chem.MolFromSmiles('c1ccccc1O')\n",
" qed = float(QED.qed(mol))\n",
" calc = Calculator(descriptors, ignore_3D=True)\n",
" md = calc(mol)\n",
" # keep numeric descriptors only\n",
" desc = {k: float(v) for k, v in md.items() if v is not None and isinstance(v, (int, float))}\n",
" print('qed:', qed, 'num_desc:', len(desc))\n",
"\n",
" # Upsert molecule with refined qed as a column; optionally also persist `desc` via JSONB/EAV patterns.\n",
" with get_session() as s:\n",
" m = s.exec(select(Molecule).where(Molecule.smiles=='c1ccccc1O')).first()\n",
" if m is None:\n",
" m = Molecule(smiles='c1ccccc1O', qed=qed)\n",
" else:\n",
" m.qed = qed\n",
" s.add(m); s.commit(); s.refresh(m)\n",
" print('Stored molecule id:', m.id)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{"cell_type":"markdown","metadata":{},"source":["# 01 — Sync CRUD Tutorial\n\n","Covers create/get/list/update/delete using the kit's helpers. Optional SQLite override for quick demo."]},
{"cell_type":"markdown","metadata":{},"source":["## 0. Install (optional)\n","Uncomment if you haven't installed the package in this env."]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# %pip install -e . pytest\n"]},
{"cell_type":"markdown","metadata":{},"source":["## 1. Optional: use in-memory SQLite for demo\n","Comment this cell if you want to use Postgres via SQL_*/PG* env vars."]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sqlmodel_pg_kit import db\n","db.cfg = db.DatabaseConfig(host='', port=0, user='', password='', database=':memory:', sslmode='disable')\n","db.engine = db.create_engine('sqlite:///:memory:', echo=False)\n"]},
{"cell_type":"markdown","metadata":{},"source":["## 2. CRUD operations (generic Repository)\n","Define a simple model and use the generic Repository for CRUD."]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from typing import Optional\n","from sqlmodel import SQLModel, Field\n","from sqlmodel_pg_kit import create_all, Repository\n","from sqlmodel_pg_kit.db import get_session\n","\n","class Hero(SQLModel, table=True):\n"," id: Optional[int] = Field(default=None, primary_key=True)\n"," name: str = Field(index=True)\n"," age: Optional[int] = None\n","\n","create_all()\n","repo = Repository(Hero)\n","with get_session() as s:\n"," h = repo.create(s, {'name': 'Alice', 'age': 20})\n"," h\n"]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["with get_session() as s:\n"," h2 = repo.get(s, h.id)\n"," h2\n"]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["with get_session() as s:\n"," h3 = repo.update(s, h.id, age=21)\n"," h3\n"]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["with get_session() as s:\n"," page = repo.list(s, page=1, size=5)\n"," [(x.id, x.name, x.age) for x in page]\n"]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["with get_session() as s:\n"," ok = repo.delete(s, h.id)\n"," ok\n"]}
],
"metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"name": "python", "pygments_lexer": "ipython3"}},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{"cell_type":"markdown","metadata":{},"source":["# 02 — Bulk Insert and Filters\n\n","Demonstrates bulk inserts and filtering with SQLModel expressions."]},
{"cell_type":"markdown","metadata":{},"source":["## 0. Install (optional)"]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# %pip install -e . pytest\n"]},
{"cell_type":"markdown","metadata":{},"source":["## 1. Optional: SQLite in-memory for demo"]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sqlmodel_pg_kit import db\n","db.cfg = db.DatabaseConfig(host='', port=0, user='', password='', database=':memory:', sslmode='disable')\n","db.engine = db.create_engine('sqlite:///:memory:', echo=False)\n"]},
{"cell_type":"markdown","metadata":{},"source":["## 2. Bulk insert and query"]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from typing import List, Optional\n","from sqlmodel import select, SQLModel, Field\n","from sqlmodel_pg_kit import create_all, Repository\n","from sqlmodel_pg_kit.db import get_session\n","\n","class Hero(SQLModel, table=True):\n"," id: Optional[int] = Field(default=None, primary_key=True)\n"," name: str = Field(index=True)\n"," age: Optional[int] = None\n","\n","create_all()\n","repo = Repository(Hero)\n","# Clean slate\n","with get_session() as s:\n"," s.exec(select(Hero)) # warmup\n"," s.execute(Hero.__table__.delete())\n"," s.commit()\n"," rows = [\n"," {'name': 'PG Hero', 'age': 1},\n"," {'name': 'PG Hero', 'age': 2},\n"," {'name': 'Bob', 'age': 30},\n"," {'name': 'Carol', 'age': 40},\n"," ]\n"," repo.bulk_insert(s, rows)\n","\n","with get_session() as s:\n"," heroes: List[Hero] = s.exec(select(Hero).where(Hero.name=='PG Hero')).all()\n"," [(h.id, h.age) for h in heroes]\n"]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["with get_session() as s:\n"," res = s.exec(select(Hero).where(Hero.age>=2).order_by(Hero.age.asc())).all()\n","[(h.name, h.age) for h in res]\n"]}
],
"metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"name": "python", "pygments_lexer": "ipython3"}},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{"cell_type":"markdown","metadata":{},"source":["# 03 — Relationships (Team ↔ Hero)\n\n","Demonstrates one-to-many relationship and eager loading with selectinload."]},
{"cell_type":"markdown","metadata":{},"source":["## 0. Install (optional)"]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# %pip install -e . pytest\n"]},
{"cell_type":"markdown","metadata":{},"source":["## 1. Optional: SQLite in-memory for demo"]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sqlmodel_pg_kit import db\n","db.cfg = db.DatabaseConfig(host='', port=0, user='', password='', database=':memory:', sslmode='disable')\n","db.engine = db.create_engine('sqlite:///:memory:', echo=False)\n"]},
{"cell_type":"markdown","metadata":{},"source":["## 2. Create Team and Hero, eager load with selectinload"]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from typing import List, Optional\n","from sqlalchemy.orm import selectinload\n","from sqlmodel import select, SQLModel, Field, Relationship\n","from sqlmodel_pg_kit import create_all\n","from sqlmodel_pg_kit.db import get_session\n","\n","class Team(SQLModel, table=True):\n"," id: Optional[int] = Field(default=None, primary_key=True)\n"," name: str = Field(index=True)\n"," heroes: List['Hero'] = Relationship(back_populates='team')\n","\n","class Hero(SQLModel, table=True):\n"," id: Optional[int] = Field(default=None, primary_key=True)\n"," name: str = Field(index=True)\n"," age: Optional[int] = None\n"," team_id: Optional[int] = Field(default=None, foreign_key='team.id')\n"," team: Optional[Team] = Relationship(back_populates='heroes')\n","\n","create_all()\n","with get_session() as s:\n"," s.execute(Hero.__table__.delete())\n"," s.execute(Team.__table__.delete())\n"," s.commit()\n"," t = Team(name='Avengers'); s.add(t); s.commit(); s.refresh(t)\n"," s.add(Hero(name='Thor', age=1500, team_id=t.id))\n"," s.add(Hero(name='Hulk', age=49, team_id=t.id))\n"," s.commit()\n","\n","stmt = select(Hero).options(selectinload(Hero.team)).order_by(Hero.id.asc())\n","with get_session() as s:\n"," heroes: List[Hero] = s.exec(stmt).all()\n","[(h.name, h.team.name if h.team else None) for h in heroes]\n"]}
],
"metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"name": "python", "pygments_lexer": "ipython3"}},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{"cell_type":"markdown","metadata":{},"source":["# 04 — Async CRUD Tutorial\n\n","Demonstrates async session usage. Includes optional SQLite async override."]},
{"cell_type":"markdown","metadata":{},"source":["## 0. Install (optional)"]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# %pip install -e . pytest\n"]},
{"cell_type":"markdown","metadata":{},"source":["## 1. Optional: SQLite async override for demo\n","If you don't have Postgres ready, configure an async SQLite engine."]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker\n","from sqlmodel_pg_kit import db, create_all\n","# Keep sync paths on SQLite in-memory for create_all\n","db.cfg = db.DatabaseConfig(host='', port=0, user='', password='', database=':memory:', sslmode='disable')\n","db.engine = db.create_engine('sqlite:///:memory:', echo=False)\n","create_all()\n","# Now override async engine/session to SQLite as well\n","db.async_engine = create_async_engine('sqlite+aiosqlite:///:memory:', echo=False)\n","db.AsyncSessionLocal = async_sessionmaker(db.async_engine, expire_on_commit=False)\n"]},
{"cell_type":"markdown","metadata":{},"source":["## 2. Async CRUD\n","Clean table, insert, and read back using async session."]},
{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import asyncio\n","from typing import Optional\n","from sqlmodel import select, SQLModel, Field\n","from sqlmodel_pg_kit import AsyncRepository, create_all\n","from sqlmodel_pg_kit.db import get_async_session\n","\n","class Hero(SQLModel, table=True):\n"," id: Optional[int] = Field(default=None, primary_key=True)\n"," name: str = Field(index=True)\n"," age: Optional[int] = None\n","\n","create_all()\n","repo = AsyncRepository(Hero)\n","\n","async def amain():\n"," async with get_async_session() as s:\n"," await s.execute(Hero.__table__.delete())\n"," await s.commit()\n"," await repo.create(s, {'name': 'Async Hero', 'age': 7})\n"," res = await s.execute(select(Hero))\n"," heroes = res.scalars().all()\n"," return [h.name for h in heroes]\n","\n","asyncio.run(amain())\n"]}
],
"metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"name": "python", "pygments_lexer": "ipython3"}},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cheminformatics Tutorial — Molecules, Datasets, CRUD & Joins\n\n",
"This notebook is a teaching version of `examples/05_cheminformatics.py`.\n",
"It demonstrates:\n",
"- Modeling molecules with descriptors (smiles, selfies, qed, sa_score)\n",
"- Linking molecules to datasets (many-to-many)\n",
"- Dataclass interop for fast inserts\n",
"- Common CRUD, filtering, eager loading, and joins\n",
"- Optional RDKit + Mordred descriptor computation (if installed)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 0. Environment (micromamba)\n",
"In your shell, activate the env before launching Jupyter:\n",
"```bash\n",
"micromamba activate sqlmodel\n",
"jupyter lab # or jupyter notebook\n",
"```\n\n",
"Optional installs inside Jupyter (uncomment to run):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# %pip install -e . pytest\n",
"# Optional cheminformatics packages:\n",
"# %pip install rdkit-pypi mordred\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Configure database connection\n",
"- For quick smoke in-memory SQLite, see the cell below.\n",
"- For PostgreSQL, ensure `SQL_*` or `PG*` env vars are set before starting Jupyter."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sqlmodel_pg_kit import db, create_all as _create_all\n",
"\n",
"# QUICK OPTION: Use SQLite in-memory for learning/demo.\n",
"# Comment these two lines out if you prefer to use Postgres via environment variables.\n",
"db.cfg = db.DatabaseConfig(host='', port=0, user='', password='', database=':memory:', sslmode='disable')\n",
"db.engine = db.create_engine('sqlite:///:memory:', echo=False)\n",
"_create_all() # create base kit models if any\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Define models & dataclass\n",
"We define `Molecule`, `Dataset`, and the link table `MoleculeDataset`.\n",
"We also provide a `MoleculeDTO` dataclass to show how to bring computed values\n",
"(e.g., from RDKit/Mordred pipelines) into SQLModel quickly."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import annotations\n",
"from dataclasses import dataclass\n",
"from datetime import datetime\n",
"from typing import List, Optional\n",
"\n",
"from sqlalchemy.orm import selectinload\n",
"from sqlmodel import SQLModel, Field, Relationship, select\n",
"from sqlmodel_pg_kit.db import get_session, engine\n",
"\n",
"class MoleculeDataset(SQLModel, table=True):\n",
" molecule_id: int = Field(foreign_key='molecule.id', primary_key=True)\n",
" dataset_id: int = Field(foreign_key='dataset.id', primary_key=True)\n",
" added_at: datetime = Field(default_factory=datetime.utcnow)\n",
"\n",
"class Molecule(SQLModel, table=True):\n",
" id: Optional[int] = Field(default=None, primary_key=True)\n",
" smiles: str = Field(index=True)\n",
" selfies: Optional[str] = Field(default=None)\n",
" qed: Optional[float] = Field(default=None, index=True)\n",
" sa_score: Optional[float] = Field(default=None, index=True)\n",
" created_at: datetime = Field(default_factory=datetime.utcnow)\n",
" updated_at: datetime = Field(default_factory=datetime.utcnow)\n",
" datasets: List['Dataset'] = Relationship(back_populates='molecules', link_model=MoleculeDataset)\n",
"\n",
"class Dataset(SQLModel, table=True):\n",
" id: Optional[int] = Field(default=None, primary_key=True)\n",
" name: str = Field(index=True)\n",
" molecules: List['Molecule'] = Relationship(back_populates='datasets', link_model=MoleculeDataset)\n",
"\n",
"@dataclass\n",
"class MoleculeDTO:\n",
" smiles: str\n",
" selfies: Optional[str] = None\n",
" qed: Optional[float] = None\n",
" sa_score: Optional[float] = None\n",
" def to_model(self) -> Molecule:\n",
" return Molecule(**vars(self))\n",
"\n",
"# Create the tables defined above\n",
"SQLModel.metadata.create_all(engine)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Clean slate (idempotent runs)\n",
"We delete existing rows to make this notebook repeatable."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with get_session() as s:\n",
" s.execute(MoleculeDataset.__table__.delete())\n",
" s.execute(Molecule.__table__.delete())\n",
" s.execute(Dataset.__table__.delete())\n",
" s.commit()\n",
"'cleaned'\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Insert molecules via dataclass\n",
"Create a few molecules as you would after computing descriptors upstream."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mols = [\n",
" MoleculeDTO(smiles='CCO', qed=0.45, sa_score=2.1),\n",
" MoleculeDTO(smiles='c1ccccc1', qed=0.76, sa_score=3.5),\n",
" MoleculeDTO(smiles='CCN(CC)CC', qed=0.62, sa_score=2.8),\n",
"]\n",
"with get_session() as s:\n",
" for dto in mols:\n",
" s.add(dto.to_model())\n",
" s.commit()\n",
"\n",
"with get_session() as s:\n",
" inserted = s.exec(select(Molecule).order_by(Molecule.id.asc())).all()\n",
"[(m.id, m.smiles, m.qed, m.sa_score) for m in inserted]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Create datasets and link molecules\n",
"Use a many-to-many link table to assign molecules to `train` or `holdout`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with get_session() as s:\n",
" ds_train = Dataset(name='train')\n",
" ds_holdout = Dataset(name='holdout')\n",
" s.add(ds_train); s.add(ds_holdout); s.commit()\n",
" s.refresh(ds_train); s.refresh(ds_holdout)\n",
" mol_list: List[Molecule] = s.exec(select(Molecule).order_by(Molecule.id.asc())).all()\n",
" links = [\n",
" MoleculeDataset(molecule_id=mol_list[0].id, dataset_id=ds_train.id),\n",
" MoleculeDataset(molecule_id=mol_list[1].id, dataset_id=ds_train.id),\n",
" MoleculeDataset(molecule_id=mol_list[2].id, dataset_id=ds_holdout.id),\n",
" ]\n",
" s.add_all(links); s.commit()\n",
"[(l.molecule_id, l.dataset_id) for l in links]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Update a descriptor (refined QED)\n",
"Typical pattern: load → modify → commit → refresh."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"with get_session() as s:\n",
" mol = s.exec(select(Molecule).where(Molecule.smiles=='CCO')).one()\n",
" mol.qed = 0.50\n",
" mol.updated_at = datetime.utcnow()\n",
" s.add(mol); s.commit(); s.refresh(mol)\n",
"(mol.id, mol.smiles, mol.qed)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Filtering and ordering\n",
"Examples: threshold on `qed` and ordering by `sa_score`; prefix search on smiles."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with get_session() as s:\n",
" hi_qed = s.exec(select(Molecule).where(Molecule.qed>=0.6).order_by(Molecule.sa_score.asc())).all()\n",
" hi_qed_view = [(m.smiles, m.qed, m.sa_score) for m in hi_qed]\n",
"\n",
"with get_session() as s:\n",
" starts_with_cc = s.exec(select(Molecule).where(Molecule.smiles.like('CC%'))).all()\n",
" starts_with_cc_view = [m.smiles for m in starts_with_cc]\n",
"\n",
"hi_qed_view, starts_with_cc_view\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Eager loading relationships (avoid N+1)\n",
"Read molecules with their datasets efficiently using `selectinload`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with get_session() as s:\n",
" stmt = select(Molecule).options(selectinload(Molecule.datasets)).order_by(Molecule.id.asc())\n",
" molecules = s.exec(stmt).all()\n",
"[(m.smiles, [d.name for d in m.datasets]) for m in molecules]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9. Join filtering\n",
"Return only molecules that belong to the `train` dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with get_session() as s:\n",
" stmt = (select(Molecule)\n",
" .join(MoleculeDataset, Molecule.id==MoleculeDataset.molecule_id)\n",
" .join(Dataset, Dataset.id==MoleculeDataset.dataset_id)\n",
" .where(Dataset.name=='train')\n",
" .order_by(Molecule.id.asc()))\n",
" train_mols = s.exec(stmt).all()\n",
"[m.smiles for m in train_mols]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 10. Delete a molecule\n",
"Load → delete → commit; verify remaining molecules."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with get_session() as s:\n",
" target = s.exec(select(Molecule).where(Molecule.smiles=='CCN(CC)CC')).one()\n",
" s.delete(target); s.commit()\n",
" left = s.exec(select(Molecule).order_by(Molecule.id.asc())).all()\n",
"[m.smiles for m in left]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 11. Optional: RDKit + Mordred computation\n",
"If installed, compute descriptors and update a molecule (e.g., refine QED)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" from rdkit import Chem\n",
" from rdkit.Chem import QED\n",
" from mordred import Calculator, descriptors\n",
" ok = True\n",
"except Exception as e:\n",
" ok = False\n",
" print('RDKit/Mordred not available; skipping.\\n', e)\n",
"\n",
"if ok:\n",
" mol = Chem.MolFromSmiles('c1ccccc1O')\n",
" qed_val = float(QED.qed(mol))\n",
" calc = Calculator(descriptors, ignore_3D=True)\n",
" md = calc(mol)\n",
" num_desc = sum(1 for _ in md.items())\n",
" print('Computed QED:', qed_val, 'Mordred descriptors:', num_desc)\n",
" with get_session() as s:\n",
" m = s.exec(select(Molecule).where(Molecule.smiles=='c1ccccc1O')).first()\n",
" if m is None:\n",
" m = Molecule(smiles='c1ccccc1O', qed=qed_val)\n",
" else:\n",
" m.qed = qed_val\n",
" s.add(m); s.commit(); s.refresh(m)\n",
" (m.id, m.smiles, m.qed)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

172
notebooks/06_csv_import.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 06 — CSV → SQLModel → Table\n\n",
"Auto-generate a SQLModel from a CSV header, import rows, and query.\n",
"Demonstrates SQLite/Postgres switching and simple filtering."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 0. Install (optional) and choose backend\n",
"- Use SQLite in-memory or file by overriding `db.engine`\n",
"- Or rely on Postgres via `SQL_*`/`PG*` env vars"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# %pip install -e . pytest\n",
"from sqlmodel_pg_kit import db\n",
"# Uncomment ONE of the following to use SQLite:\n",
"# db.engine = db.create_engine('sqlite:///:memory:', echo=False)\n",
"# db.engine = db.create_engine('sqlite:///./demo.db', echo=False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Build model from CSV and insert rows\n",
"We use the sample CSV at `data/molecules_sample.csv`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Parameters\n",
"Set CSV path, optional class/table names, null sentinels, type overrides, and column renames."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# CSV path and optional names\n",
"csv_path = 'data/molecules_sample.csv'\n",
"class_name_override = None # e.g., 'Molecules'\n",
"table_name_override = None # e.g., 'molecules'\n",
"\n",
"# Null sentinels (in addition to default: '', na, nan, none, null)\n",
"custom_nulls = ['N/A']\n",
"\n",
"# Type overrides: name -> type (bool/int/float/str)\n",
"type_overrides = { # e.g., 'count': int, 'qed': float\n",
" # 'count': int,\n",
"} \n",
"\n",
"# Rename mappings: original header -> new name before sanitization\n",
"rename_map = { # e.g., 'sa': 'sa_score'\n",
" # 'sa': 'sa_score',\n",
"}\n",
"\n",
"# Columns to index after import (B-Tree)\n",
"index_columns = ['qed', 'sa_score']\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sqlmodel_pg_kit.csv_import import build_model_from_csv, insert_rows, create_indexes\n",
"from sqlmodel_pg_kit import create_all\n",
"from sqlmodel_pg_kit.db import get_session\n",
"\n",
"spec, rows = build_model_from_csv(\n",
" csv_path,\n",
" class_name=class_name_override,\n",
" table_name=table_name_override,\n",
" null_values=custom_nulls,\n",
" type_overrides=type_overrides,\n",
" rename_map=rename_map,\n",
" warn_on_nulls=True,\n",
")\n",
"spec.model, spec.table_name, spec.columns\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create table and insert\n",
"create_all()\n",
"with get_session() as s:\n",
" n = insert_rows(spec.model, rows, s)\n",
"n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Optionally create B-Tree indexes on selected columns\n",
"from sqlmodel_pg_kit import db\n",
"created = create_indexes(spec.model, index_columns, db.engine)\n",
"created\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Query a few rows and filters\n",
"Use SQLModel/SQLAlchemy expressions to filter by inferred columns."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sqlmodel import select\n",
"\n",
"with get_session() as s:\n",
" all_rows = s.exec(select(spec.model).order_by(spec.model.id.asc())).all()\n",
"[(r.id, r.smiles, r.qed, r.sa_score, r.active) for r in all_rows]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Filter: high QED and active\n",
"with get_session() as s:\n",
" hi = s.exec(select(spec.model).where((spec.model.qed>=0.6) & (spec.model.active==True))).all()\n",
"[(r.smiles, r.qed, r.active) for r in hi]\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

44
pyproject.toml Normal file
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[project]
name = "sqlmodel-pg-kit"
version = "0.1.0"
description = "Reusable SQLModel + PostgreSQL kit: config dataclass, engines, CRUD, CLI."
readme = "README.md"
license = { text = "MIT" }
requires-python = ">=3.10"
authors = [{ name = "Your Name", email = "you@example.com" }]
keywords = ["sqlmodel", "sqlalchemy", "postgresql", "orm", "crud"]
classifiers = [
"Programming Language :: Python :: 3 :: Only",
"License :: OSI Approved :: MIT License",
"Topic :: Database",
]
dependencies = [
"sqlmodel>=0.0.16",
"psycopg[binary]>=3.1",
"asyncpg>=0.29",
"alembic>=1.13",
"pydantic>=2.6",
]
[project.optional-dependencies]
fastapi = ["fastapi>=0.111", "uvicorn>=0.30"]
pgvector = ["pgvector>=0.2"]
[project.scripts]
sqlmodel-pg-kit = "sqlmodel_pg_kit.cli:main"
[build-system]
requires = ["hatchling>=1.25"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.sdist]
include = ["src/sqlmodel_pg_kit", "README.md", "LICENSE", "CHANGELOG.md", "conda/meta.yaml"]
[tool.hatch.build.targets.wheel]
packages = ["src/sqlmodel_pg_kit"]
[tool.pytest.ini_options]
markers = [
"integration: tests that require a running external Postgres",
]

17
src/sqlmodel_pg_kit/__init__.py Normal file
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from .config import DatabaseConfig
from .db import engine, async_engine, get_session, get_async_session, create_all
from .crud import Repository, AsyncRepository
from .csv_import import build_model_from_csv, create_indexes
__all__ = [
"DatabaseConfig",
"engine",
"async_engine",
"get_session",
"get_async_session",
"create_all",
"Repository",
"AsyncRepository",
"build_model_from_csv",
"create_indexes",
]

56
src/sqlmodel_pg_kit/config.py Normal file
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from dataclasses import dataclass
from urllib.parse import quote_plus
import os
@dataclass(frozen=True)
class DatabaseConfig:
host: str = "localhost"
port: int = 5432
user: str = "appuser"
password: str = "changeme"
database: str = "appdb"
sslmode: str = "require" # "disable" / "require" / "verify-full"
pool_size: int = 10
max_overflow: int = 20
echo: bool = False
def sync_url(self) -> str:
pw = quote_plus(self.password)
return f"postgresql+psycopg://{self.user}:{pw}@{self.host}:{self.port}/{self.database}?sslmode={self.sslmode}"
def async_url(self) -> str:
pw = quote_plus(self.password)
return f"postgresql+asyncpg://{self.user}:{pw}@{self.host}:{self.port}/{self.database}?sslmode={self.sslmode}"
@staticmethod
def _getenv(name: str, alt: str | None = None, default: str | None = None) -> str | None:
# Prefer primary name, then alt, else default
val = os.getenv(name)
if val is None and alt:
val = os.getenv(alt)
return val if val is not None else default
@classmethod
def from_env(cls) -> "DatabaseConfig":
host = cls._getenv("SQL_HOST", "PGHOST", "localhost")
port_str = cls._getenv("SQL_PORT", "PGPORT", "5432")
user = cls._getenv("SQL_USER", "PGUSER", "appuser")
password = cls._getenv("SQL_PASSWORD", "PGPASSWORD", "changeme")
database = cls._getenv("SQL_DATABASE", "PGDATABASE", "appdb")
sslmode = cls._getenv("SQL_SSLMODE", default="require")
pool_size = int(cls._getenv("SQL_POOL_SIZE", default="10"))
max_overflow = int(cls._getenv("SQL_MAX_OVERFLOW", default="20"))
echo_raw = cls._getenv("SQL_ECHO", default="0")
echo = echo_raw.lower() in {"1", "true", "yes", "on"}
return cls(
host=host or "localhost",
port=int(port_str or 5432),
user=user or "appuser",
password=password or "changeme",
database=database or "appdb",
sslmode=sslmode or "require",
pool_size=pool_size,
max_overflow=max_overflow,
echo=echo,
)

141
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from __future__ import annotations
from typing import Any, Dict, Generic, Iterable, List, Optional, Sequence, Tuple, Type, TypeVar
from sqlalchemy import insert
from sqlmodel import SQLModel, select
T = TypeVar("T", bound=SQLModel)
class Repository(Generic[T]):
"""Generic synchronous repository for SQLModel models.
Example:
class User(SQLModel, table=True):
...
repo = Repository(User)
with get_session() as s:
user = repo.create(s, {"name": "Alice"})
"""
def __init__(self, model: Type[T]):
self.model = model
# Create ---------------------------------------------------------------
def create(self, session, data: T | Dict[str, Any]) -> T:
obj = data if isinstance(data, self.model) else self.model(**data) # type: ignore[arg-type]
session.add(obj)
session.commit()
session.refresh(obj)
return obj
def bulk_insert(self, session, rows: Iterable[Dict[str, Any]]) -> int:
stmt = insert(self.model).values(list(rows))
res = session.exec(stmt)
session.commit()
return res.rowcount or 0
# Read ----------------------------------------------------------------
def get(self, session, pk: Any) -> Optional[T]:
return session.get(self.model, pk)
def list(
self,
session,
where: Optional[Any] = None,
order_by: Optional[Sequence[Any]] = None,
page: int = 1,
size: int = 20,
) -> List[T]:
off = (page - 1) * size
stmt = select(self.model)
if where is not None:
stmt = stmt.where(where)
if order_by:
for ob in order_by:
stmt = stmt.order_by(ob)
stmt = stmt.offset(off).limit(size)
return session.exec(stmt).all()
# Update ---------------------------------------------------------------
def update(self, session, obj_or_pk: Any, **fields) -> Optional[T]:
obj = obj_or_pk if isinstance(obj_or_pk, self.model) else session.get(self.model, obj_or_pk)
if not obj:
return None
for k, v in fields.items():
setattr(obj, k, v)
session.add(obj)
session.commit()
session.refresh(obj)
return obj
# Delete ---------------------------------------------------------------
def delete(self, session, obj_or_pk: Any) -> bool:
obj = obj_or_pk if isinstance(obj_or_pk, self.model) else session.get(self.model, obj_or_pk)
if not obj:
return False
session.delete(obj)
session.commit()
return True
class AsyncRepository(Generic[T]):
"""Generic asynchronous repository for SQLModel models."""
def __init__(self, model: Type[T]):
self.model = model
async def create(self, session, data: T | Dict[str, Any]) -> T:
obj = data if isinstance(data, self.model) else self.model(**data) # type: ignore[arg-type]
session.add(obj)
await session.commit()
await session.refresh(obj)
return obj
async def bulk_insert(self, session, rows: Iterable[Dict[str, Any]]) -> int:
stmt = insert(self.model).values(list(rows))
res = await session.execute(stmt)
await session.commit()
return res.rowcount or 0
async def get(self, session, pk: Any) -> Optional[T]:
return await session.get(self.model, pk)
async def list(
self,
session,
where: Optional[Any] = None,
order_by: Optional[Sequence[Any]] = None,
page: int = 1,
size: int = 20,
) -> List[T]:
off = (page - 1) * size
stmt = select(self.model)
if where is not None:
stmt = stmt.where(where)
if order_by:
for ob in order_by:
stmt = stmt.order_by(ob)
stmt = stmt.offset(off).limit(size)
res = await session.execute(stmt)
return list(res.scalars().all())
async def update(self, session, obj_or_pk: Any, **fields) -> Optional[T]:
obj = obj_or_pk if isinstance(obj_or_pk, self.model) else await session.get(self.model, obj_or_pk)
if not obj:
return None
for k, v in fields.items():
setattr(obj, k, v)
session.add(obj)
await session.commit()
await session.refresh(obj)
return obj
async def delete(self, session, obj_or_pk: Any) -> bool:
obj = obj_or_pk if isinstance(obj_or_pk, self.model) else await session.get(self.model, obj_or_pk)
if not obj:
return False
await session.delete(obj)
await session.commit()
return True

247
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from __future__ import annotations
import csv
import keyword
import re
from dataclasses import dataclass
import warnings
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
from sqlmodel import SQLModel, Field
_NULL_SET = {"", "na", "nan", "none", "null"}
def _sanitize_identifier(name: str) -> str:
s = re.sub(r"\W+", "_", name.strip())
s = re.sub(r"_+", "_", s).strip("_")
if not s:
s = "col"
if s[0].isdigit():
s = f"_{s}"
if keyword.iskeyword(s):
s = f"{s}_"
return s
def _to_camel(name: str) -> str:
parts = re.split(r"\W+|_+", name)
return "".join(p.capitalize() for p in parts if p)
def _to_snake(name: str) -> str:
name = re.sub(r"\W+", "_", name)
s1 = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", name)
s2 = re.sub("([a-z0-9])([A-Z])", r"\1_\2", s1)
return re.sub(r"_+", "_", s2).lower().strip("_")
def _infer_type(values: Iterable[str], nulls: Iterable[str]) -> type:
has_val = False
all_bool = True
all_int = True
all_float = True
for raw in values:
v = (raw or "").strip()
if v.lower() in nulls:
continue
has_val = True
vl = v.lower()
if vl in {"true", "false", "1", "0", "yes", "no"}:
pass
else:
all_bool = False
# int?
try:
int(v)
except Exception:
all_int = False
# float?
try:
float(v)
except Exception:
all_float = False
if not has_val:
return str
if all_bool:
return bool
if all_int:
return int
if all_float:
return float
return str
def _cast_value(val: str, typ: type, nulls: Iterable[str], *, col: str = "", row_index: int = -1) -> Any:
if val is None:
return None
v = str(val).strip()
if v == "" or v.lower() in nulls:
return None
if typ is bool:
return v.lower() in {"true", "1", "yes"}
if typ is int:
try:
return int(v)
except Exception as e:
raise ValueError(f"Row {row_index}: column '{col}' value '{v}' cannot be cast to int") from e
if typ is float:
try:
return float(v)
except Exception as e:
raise ValueError(f"Row {row_index}: column '{col}' value '{v}' cannot be cast to float") from e
return v
@dataclass
class CsvModelSpec:
model: Type[SQLModel]
table_name: str
columns: Dict[str, type]
def build_model_from_csv(
csv_path: str,
class_name: Optional[str] = None,
table_name: Optional[str] = None,
*,
null_values: Optional[Iterable[str]] = None,
type_overrides: Optional[Dict[str, type]] = None,
rename_map: Optional[Dict[str, str]] = None,
warn_on_nulls: bool = True,
) -> Tuple[CsvModelSpec, List[Dict[str, Any]]]:
"""Read CSV, infer types, generate a SQLModel subclass dynamically, and return rows.
- Class name defaults to CSV stem in CamelCase.
- Table name defaults to snake_case of class name.
- Adds an auto-increment integer primary key column `id`.
"""
p = Path(csv_path)
if not class_name:
class_name = _to_camel(p.stem)
if not table_name:
table_name = _to_snake(class_name)
with open(csv_path, newline="", encoding="utf-8") as f:
reader = csv.DictReader(f)
raw_headers = reader.fieldnames or []
if not raw_headers:
raise ValueError("CSV has no header row")
# Apply rename map (if provided) to raw headers
rename_map = rename_map or {}
tmp_headers = [rename_map.get(h, h) for h in raw_headers]
headers = [_sanitize_identifier(h) for h in tmp_headers]
rows_raw = list(reader)
# Build mapping original->final
mapping: Dict[str, str] = {orig: new for orig, new in zip(raw_headers, headers)}
# Ensure no duplicates after rename/sanitize
if len(set(headers)) != len(headers):
raise ValueError(f"Duplicate column names after rename/sanitize: {headers}")
# Normalize null set
nulls = set((null_values or [])) | _NULL_SET
nulls = {x.lower() for x in nulls}
# Infer types per column
col_values = {mapping[o]: [] for o in raw_headers}
for r in rows_raw:
for h_raw in raw_headers:
col_values[mapping[h_raw]].append(r.get(h_raw, ""))
inferred: Dict[str, type] = {h: _infer_type(vals, nulls) for h, vals in col_values.items()}
# Apply type overrides (keys may refer to original, renamed, or sanitized)
if type_overrides:
lower_map = {k.lower(): v for k, v in mapping.items()}
for key, typ in type_overrides.items():
if typ not in (bool, int, float, str):
raise ValueError(f"Unsupported type override for '{key}': {typ}")
k_final = None
if key in inferred:
k_final = key
elif key in mapping:
k_final = mapping[key]
elif key.lower() in lower_map:
k_final = lower_map[key.lower()]
if not k_final:
raise ValueError(f"type_overrides key '{key}' not found in CSV headers {raw_headers}")
inferred[k_final] = typ
# Build class dynamically via exec to retain SQLModel 'table=True' semantics
ns: Dict[str, Any] = {"SQLModel": SQLModel, "Field": Field, "Optional": Optional}
fields_code = [
" id: Optional[int] = Field(default=None, primary_key=True)",
]
for h, typ in inferred.items():
# map python type to annotation text
if typ is bool:
ann = "bool"
elif typ is int:
ann = "int"
elif typ is float:
ann = "float"
else:
ann = "str"
fields_code.append(f" {h}: Optional[{ann}] = None")
code = [
f"class {class_name}(SQLModel, table=True):",
f" __tablename__ = '{table_name}'",
*fields_code,
]
src = "\n".join(code)
exec_env = dict(ns)
exec(src, exec_env)
model = exec_env[class_name]
# Cast rows to inferred types with sanitized keys
cast_rows: List[Dict[str, Any]] = []
null_counts: Dict[str, int] = {h: 0 for h in inferred.keys()}
for idx, r in enumerate(rows_raw, start=2): # 1-based header, so first data at line 2
item: Dict[str, Any] = {}
for h_raw in raw_headers:
h = mapping[h_raw]
raw_val = r.get(h_raw, "")
val = _cast_value(raw_val, inferred[h], nulls, col=h, row_index=idx)
if val is None:
null_counts[h] += 1
item[h] = val
cast_rows.append(item)
if warn_on_nulls:
issues = {k: v for k, v in null_counts.items() if v}
if issues:
warnings.warn(f"Nulls detected by column: {issues}")
return CsvModelSpec(model=model, table_name=table_name, columns=inferred), cast_rows
def insert_rows(model: Type[SQLModel], rows: List[Dict[str, Any]], session) -> int:
from .crud import Repository
repo = Repository(model)
# chunked bulk insert to avoid very large statements
total = 0
chunk = 1000
for i in range(0, len(rows), chunk):
total += repo.bulk_insert(session, rows[i : i + chunk])
return total
def create_indexes(model: Type[SQLModel], columns: Iterable[str], engine) -> List[str]:
"""Create B-Tree indexes on given columns (if they exist). Returns list of created index names."""
from sqlalchemy import Index
created: List[str] = []
for col in columns:
if not hasattr(model, col):
raise ValueError(
f"Index column '{col}' not found on model '{model.__name__}'. Available: {list(model.model_fields.keys())}"
)
idx_name = f"ix_{model.__tablename__}_{col}"
idx = Index(idx_name, getattr(model.__table__.c, col))
idx.create(bind=engine, checkfirst=True)
created.append(idx_name)
return created

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from contextlib import contextmanager, asynccontextmanager
from sqlmodel import SQLModel, create_engine, Session
from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker
from .config import DatabaseConfig
cfg = DatabaseConfig.from_env() # 生产中可通过环境变量构造
engine = create_engine(
cfg.sync_url(), pool_size=cfg.pool_size, max_overflow=cfg.max_overflow,
pool_pre_ping=True, echo=cfg.echo,
)
@contextmanager
def get_session():
with Session(engine) as session:
yield session
async_engine = create_async_engine(cfg.async_url(), echo=cfg.echo, pool_pre_ping=True)
AsyncSessionLocal = async_sessionmaker(async_engine, expire_on_commit=False)
@asynccontextmanager
async def get_async_session():
async with AsyncSessionLocal() as session:
yield session
def create_all():
SQLModel.metadata.create_all(engine)

7
src/sqlmodel_pg_kit/models.py Normal file
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"""
This module intentionally left minimal.
Define your own SQLModel models in your application code or notebooks,
then call `sqlmodel_pg_kit.create_all()` to create tables and use
`Repository`/`AsyncRepository` for CRUD.
"""

70
tests/test_pg_integration.py Normal file
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import os
import socket
import time
from typing import Optional
import pytest
from sqlmodel import SQLModel, Field
def _wait_for_port(host: str, port: int, deadline_s: float = 5.0) -> bool:
start = time.time()
while time.time() - start < deadline_s:
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.settimeout(1.0)
try:
s.connect((host, port))
return True
except OSError:
time.sleep(0.25)
return False
@pytest.mark.integration
def test_postgres_connection_and_crud(monkeypatch):
# Prefer env to locate DB; fall back to localhost:5433
host = os.getenv("SQL_HOST") or os.getenv("PGHOST") or "127.0.0.1"
port = int(os.getenv("SQL_PORT") or os.getenv("PGPORT") or 5433)
if not _wait_for_port(host, port):
pytest.skip(f"Postgres not reachable at {host}:{port}; skip integration test")
from sqlmodel_pg_kit import db
from sqlmodel_pg_kit import create_all, Repository
from sqlmodel_pg_kit.db import get_session
class Hero(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
name: str
age: Optional[int] = None
user = os.getenv("SQL_USER") or os.getenv("PGUSER") or "postgres"
password = os.getenv("SQL_PASSWORD") or os.getenv("PGPASSWORD") or "change-me-strong"
database = os.getenv("SQL_DATABASE") or os.getenv("PGDATABASE") or "postgres"
sslmode = os.getenv("SQL_SSLMODE") or "disable"
cfg = db.DatabaseConfig(
host=host,
port=port,
user=user,
password=password,
database=database,
sslmode=sslmode,
echo=False,
)
monkeypatch.setattr(db, "cfg", cfg)
monkeypatch.setattr(db, "engine", db.create_engine(cfg.sync_url(), echo=False))
# Try a few retries in case container just started
for _ in range(10):
try:
create_all()
break
except Exception: # pragma: no cover - only on slow CI
time.sleep(0.5)
repo = Repository(Hero)
with get_session() as s:
repo.create(s, {"name": "PG Hero", "age": 1})
names = [h.name for h in repo.list(s)]
assert "PG Hero" in names

27
tests/test_smoke.py Normal file
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@@ -0,0 +1,27 @@
from typing import Optional
from sqlmodel import SQLModel, Field
from sqlmodel_pg_kit import create_all, Repository
def test_smoke(tmp_path, monkeypatch):
# 用 SQLite 临时库跑一遍（替换 cfg 同步 URL）
from sqlmodel_pg_kit import db
from sqlmodel_pg_kit.db import get_session
class Hero(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
name: str = Field(index=True)
age: Optional[int] = None
monkeypatch.setattr(db, "cfg", db.DatabaseConfig(
host="", port=0, user="", password="", database=":memory:", sslmode="disable"
))
monkeypatch.setattr(db, "engine", db.create_engine("sqlite:///:memory:", echo=False))
create_all()
repo = Repository(Hero)
with get_session() as s:
repo.create(s, {"name": "Iron Man", "age": 45})
names = [h.name for h in repo.list(s)]
assert names == ["Iron Man"]

814
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