alchemy-kit

A typed, reflection-generated, multi-dialect, DataFrame-first SQL toolkit for Python. It reflects your database into typed models, builds queries through a fluent, autocomplete-friendly API, and returns pandas DataFrames with pandera validation — not a classic object-graph ORM.

What it solves

Writing SQL against a real database in Python usually means one of two painful extremes: raw strings that no editor understands and that break silently when the schema changes, or a heavyweight object-graph ORM that hides the SQL and fights you the moment you need analytics-style queries.

alchemy-kit sits in between:

• Your schema becomes typed Python. It reflects your live database and generates one model per table/view/procedure, so column names, types and nullability are known to your editor and type checker.
• Queries are built, not concatenated. A fluent builder produces the SQL; invalid column references and cross-connection mistakes are caught before the query ever runs.
• Results are DataFrames. Every query returns a pandas DataFrame, so it drops straight into analysis, ETL and data pipelines.
• One API, many databases. The same code targets SQL Server, PostgreSQL, MySQL, MariaDB, Oracle and SQLite; dialect differences are handled for you.
• The library owns transactions. Commit/rollback boundaries are managed internally, including all-or-nothing multi-statement runs.

In practice that means you work with tables and explicit joins that return DataFrames, rather than lazily-loaded object graphs.

Supported databases

SQL Server · PostgreSQL · MySQL · MariaDB · Oracle · SQLite

Installation

pip install alchemy-kit

Database drivers are optional extras — install the one you need, e.g.:

pip install "alchemy-kit[mssql]"

Requires Python 3.12+.

Quick start

1. Generate typed models from your database

build reflects the schema and writes a typed model module per object into the target directory.

from pathlib import Path

import sqlalchemy

from alchemy_kit.connect import ConnectionInfo
from alchemy_kit.model import build

conn = ConnectionInfo(sqlalchemy.make_url("sqlite:///shop.db"))

build(conn, Path("./models"), clear_result_dir=True)

For richer connections (auth methods, drivers, TLS) use the info_builder helpers instead of a raw URL, e.g. info_builder.from_values_mssql(...).

2. Query with the typed builder

Open a connection, get a typed handle on a table, and build a SELECT. Every query returns a (row_count, DataFrame) pair.

import sqlalchemy

from alchemy_kit.connect import ConnectionInfo, EngineManager
from alchemy_kit.builder import SelectBuilder

from models.items_MODULE import items  # generated in step 1

conn = ConnectionInfo(sqlalchemy.make_url("sqlite:///shop.db"))

with EngineManager(None) as manager:
    handler = manager.create_engine(conn)

    # A typed handle on the table, bound to this connection and dialect.
    table = handler.get_unit(items)

    # SELECT name, price FROM items WHERE price > 1 ORDER BY price DESC
    _, df = (
        SelectBuilder(table.name, table.price, from_=table)
          .where(table.price > 1)
          .order_by(table.price.desc())
    ).run()
    print(df)

table.price, table.name, ... are the model's columns, so typos and type mismatches are flagged by your editor. The builder also covers join, group_by, having, distinct, limit/offset, paginate, aggregates (sum, avg, count, ...) and window functions (row_number, rank, lag/lead, over).

3. Write data

from alchemy_kit.builder import InsertBuilder, UpdateBuilder, DeleteBuilder

# INSERT a single row — (column, value) pairs, like set_values above
InsertBuilder(table).from_values((table.id_1, 1), (table.name, "bolt"), (table.price, 0.5)).run()

# Bulk INSERT a DataFrame, chunked
InsertBuilder(table).from_dataframe(df).run(chunk_size=1_000)

# UPDATE items SET price = price * 2 WHERE name = 'bolt'
UpdateBuilder(table).set_values((table.price, table.price * 2)).where(table.name == "bolt").run()

# DELETE FROM items WHERE price < 1
DeleteBuilder(table).where(table.price < 1).run()

4. Call a stored procedure

Generated procedure models run the same way — the arguments are positional.

from models.recount_stock_CALL import recount_stock  # generated in step 1

proc = handler.get_unit(recount_stock)
_, df = proc.run(42, True)

Validate a DataFrame against a model

Generated models are pandera schemas, so any DataFrame can be validated against the table's real shape:

items.validate(df)

Documentation

In-depth developer documentation — covering the architecture and each component in detail — lives under docs/. It is still a work in progress and will be filled out soon.

Project status

alchemy-kit is under active development. See docs/STATUS.md for exactly which SQL features are implemented, what is planned (e.g. MERGE/UPSERT, persistent-object DDL), and open design questions.