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🐍 Here lies detailed Python module documentation

All Modules

Oxen is a lightning fast data version control system for structured and unstructured machine learning datasets.

🐂 What is Oxen?

How to install the Oxen client, server, or python package.

⚒️ Installation

🐮 Learn The Basics

If you are familiar with git, oxen should be an easy learning curve.

💻 Command Line Interface

Learn how to get started with the oxenai python package.

🐍 Python

The oxen-server binary is a remote server that can be used to run an API to sync the data to.

📡 Oxen Server

🔥 Performance

Oxen allows you to interact with your data without having to download the entire dataset locally.

🧩 Partial Clones

OxenHub enables your team to version, explore, and collaborate on your datasets.

🌎 Web Hub

Oxen makes it easy to work with tabular data as well as other data formats.

📊 Data Frames

🚀 Model Inference

You can now edit large datasets straight from Oxen.AI's website without downloading the dataset.

📝 Labeling Data

Writing SQL queries has never been easier with Oxen.AI's Text2SQL feature. All you need is a natural language question and you instantly get a SQL query generated for you which you can edit however you'd like.

🔍 Text2SQL

Sentiment Analysis is the process of categorizing the overall feeling and opinion of a piece of text. Usually it is broken down into multiple categories such as positive, negative, and neutral.

🧐 Sentiment Analysis

Text translation is the process of translating text into another language. This process has never been easier with LLMs commonly supporting multiple languages such as Spanish, French, and Italian.

📝 Text Translation

**Retrieval-Augmented Generation (RAG)** is the process of retrieving information that the AI does not have in its training data to answer a question. RAG is usually used to answer customer service questions, analyze legal documents, answer internal business questions, and much more.

🧠 RAG: Extracting Answers

Quickly find changes in your datasets with Oxen.ai

Dataset Diffs

Oxen.ai helps you compare results from your machine learning models.

Comparing Models

Managing and validating data schemas is an important part of making robust data pipelines.

Schemas

After you push your data to an [oxen-server](/getting-started/oxen-server), you can use remote repositories to interact with your data.

Remote Repositories

Workspaces allows you to interact with your data without having to download the entire dataset locally.

Python API

​Clone

​Initialize Repository

​Configure User

​Setup Auth

​Local Repository