ObjectBox Python is a lightweight yet powerful on-device object and vector database. Store Python objects and vectors directly with an easy-to-use CRUD API while enjoying exceptional speed and efficiency. And because it's an embedded database, there's no setup required.

Its advanced vector search empowers on-device AI applications including RAG, generative AI, and similarity searches.

The ObjectBox database delivers high-performance on commodity hardware - locally, on-device. On top, as an offline-first solution, ObjectBox makes sure your app reliably works offline as well as online (via Sync).

Table of Contents

• Feature Highlights
• Code Example (CRUD - Create, Read, Update, Delete)
• Getting Started
• Alpha Notes
• Help wanted
• Feedback
• License

🏁 On-device vector database - for AI apps that work any place.
🏁 High performance - superfast response rates enabling real-time applications.
🪂 ACID compliant - Atomic, Consistent, Isolated, Durable.
🌱 Scalable - grows with your app, handling millions of objects with ease.
💚 Sustainable - frugal on CPU, Memory and battery / power use, reducing CO2 emissions.
💐 Queries - filter data as needed, even across relations.
💻 Multiplatform - Get native speed on your favorite platforms.\

• Linux x86-64 (64-bit)
• Linux ARMv6hf (e.g. Raspberry PI Zero)
• Linux ARMv7hf (e.g. Raspberry PI 3)
• Linux ARMv8 (e.g. Raspberry PI 4, 5, etc.)
• MacOS x86-64 and arm64 (Intel 64-bit and Apple Silicon)
• Windows x86-64 (64-bit)

What does using ObjectBox in Python look like?

from objectbox import Entity, Id, Store, String

@Entity()
class Person:
    id = Id
    name = String

# The ObjectBox Store represents a database; keep it around...
store = Store()

# Get a box for the "Person" entity; a Box is the main interaction point with objects and the database.
box = store.box(Person)

person = Person(name = "Joe Green")
id = box.put(person)  # Create
person = box.get(id)  # Read
person.name = "Joe Black"
box.put(person)       # Update
box.remove(person)    # Delete

Ready for more? Check the example folder.

Latest version: 4.0.0 (2024-05-28)

To install or update the latest version of ObjectBox, run this:

pip install --upgrade objectbox

Now you are ready to use ObjectBox in your Python project.

Head over to the ObjectBox documentation and learn how to setup your first entity classes.

Do you prefer to dive right into working examples? We have you covered in the example folder. It comes with a task list application and a vector search example using cities (CLI app and Jupyter notebook). For AI developers , we provide an "ollama" example, which integrates a local LLM (via ollama) with ObjectBox to manage and search embeddings effectively.

ObjectBox for Python is open to contributions. The ObjectBox team will try its best to guide you and answer questions. See CONTRIBUTING.md to get started.

We are looking for your feedback! Please let us know what you think about ObjectBox for Python and how we can improve it.

Copyright 2019-2024 ObjectBox Ltd. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.