savekit 2.0.2

A lightweight, persistent key-value storage toolkit for Python projects, using SQLite via SQLAlchemy. Supports storing primitive types, complex objects, and Pydantic models with full context manager support and project-root storage.

SaveKit

SaveKit is a lightweight persistent key–value storage system built on top of SQLite using SQLAlchemy. It is designed for Python projects that need to store configuration, state, or small amounts of data reliably without additional infrastructure.

Key Features

• Local persistence using SQLite
• Simple key–value interface
• Support for primitive types and JSON-serializable structures
• Native support for Pydantic models
• Safe SQLAlchemy session handling
• Optional context manager usage
• Export and import data as JSON
• Automatic storage inside a project-level store directory

Architecture

SaveKit is composed of two main components:

• StoreItem: A SQLAlchemy model representing a persisted key–value entry.
• SaveKit: A service class responsible for all persistence operations.

All database and serialization logic is fully encapsulated inside SaveKit, keeping consumer code clean and free of persistence concerns.

Requirements

• Python 3.10+
• SQLAlchemy
• Pydantic

Initialization Behavior

When a SaveKit instance is created:

• The project root is resolved using os.getcwd()
• A directory named store is created if it does not exist
• A SQLite database file is created inside that directory
• Required tables are created automatically

The database name can be customized during initialization.

Example

from savekit import SaveKit

store = SaveKit()

store = SaveKit(db_name="my_app_data")

Usage Overview

SaveKit supports two usage patterns:

• Direct usage, where sessions are created and closed automatically
• Context manager usage for grouped operations within a single session

Direct Usage Example

store = SaveKit()

store.set_item("theme", "dark")
value = store.get_item("theme")

Context Manager Usage Example

with SaveKit() as store:
    store.set_item("counter", 10)
    store.set_item("enabled", True)

Supported Operations

Store a Value

Supports storing:

• Primitive values
• Dictionaries and lists
• Pydantic BaseModel instances

store.set_item("count", 5)
store.set_item("settings", {"retry": 3, "timeout": 30})

Storing a Pydantic Model

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str

user = User(id=1, name="Alice")
store.set_item("user", user)

Retrieve a Value

• Fetch values by key
• Provide a default value if the key does not exist
• Optionally parse the stored value into a Pydantic model

theme = store.get_item("theme", default="light")

Retrieve as Pydantic Model

user = store.get_item("user", model=User)

Delete a Value

Removes a specific key from the store.

store.delete_item("theme")

Retrieve All Values

Returns the entire store content as a dictionary.

data = store.get_all_items()

Clear Store

Deletes all entries from the store.

store.clear_store()

Export Data

Exports all stored data to a JSON file.

store.export_store("backup.json")

Import Data

Imports data from a JSON file, replacing the current store content.

store.import_store("backup.json")

Reload Store

Reloads and returns all items from the database.

data = store.reload_store()

Error Handling

• Invalid serialization raises ValueError
• Database-related failures raise RuntimeError
• Sessions are always closed safely, even on errors

Recommended Use Cases

• Persistent application configuration
• Simple local cache
• Process state storage
• Internal tools and prototypes
• CLI applications and productive scripts

Limitations

• Not designed for high concurrency workloads
• Not a replacement for a full relational database
• Intended for lightweight, local persistence only

License

Free to use for personal and professional projects.