Redis AI Java Resources

A self-contained Docker Application that provides a set of Jupyter Notebooks and associated utilities to work with Java and Java AI Libraries

Overview

This repository provides a Docker-based environment with Jupyter notebooks demonstrating how to create your own Data Science/ML/GenAI learning platform

Getting Started

Clone the Repository

git clone git@github.com:bsbodden/data-science-with-java.git
cd data-science-with-java.git

Start the Docker Containers

This will start two containers:

• jupyter: A Jupyter notebook server with Java and Python kernels
• redis-java: A Redis instance bundled with RedisInsight GUI

docker compose up

Launch Jupyter Notebook

1. Monitor the Docker logs for the Jupyter server startup message
2. Look for a URL like http://127.0.0.1:8888/lab?token=<your_token>
3. Open this URL in your browser
4. Navigate to the notebooks directory and open one of the example notebooks

Adding Java Libraries

Java dependencies can be added directly in the notebooks using the %maven magic command:

%maven group:artifact:version

For example:

%maven org.apache.commons:commons-math3:3.6.1

Creating New Notebooks

1. Simply create new .ipynb files in the notebooks directory
2. They will automatically be available in the Jupyter interface
3. No configuration changes needed

Using RedisInsight

RedisInsight is a visual tool for Redis that provides an intuitive interface to:

• Browse and interact with your Redis data
• Run queries and commands
• Analyze memory usage
• Monitor performance metrics

To access RedisInsight:

1. Once the containers are running, open your browser to http://localhost:8001
2. On first launch, you'll need to connect to your Redis instance
3. Use the following connection details:
   • Host: redis
   • Port: 6379
   • Username: default
   • Password: redis123 (if you kept the default from docker-compose.yml)

RedisInsight makes it easy to visualize and explore the vector data you'll be working with in the notebooks.

Technical Details

• Uses Java 21 for all Java code execution
• Built on the Jupyter Docker Stacks images
• Includes both Java and Python kernels
• Mounts local directories to persist your work