README.md

November 24, 2025 · View on GitHub

Redis AI Java Resources

✨ Java-based code examples, notebooks, and resources for using Redis in AI and ML applications. ✨

Setup | Running the Project | Notebooks | Project Structure | Implementation Details

Setup

This project uses Docker Compose to set up a complete environment for running Java-based AI applications with Redis. The environment includes:

A Jupyter Notebook server with Java kernel support (For notebooks that based on Java)
Redis Stack (includes Redis and RedisInsight)
Pre-installed dependencies for AI/ML workloads

Prerequisites

Docker and Docker Compose
OpenAI API key (for notebooks that use OpenAI services)

Environment Configuration

Create a .env file in the project root with your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key_here

Running the Project

Clone the repository (if you haven't already):

git clone https://github.com/redis-developer/redis-ai-resources.git
cd redis-ai-resources/java-resources

Start the Docker containers:
```
docker-compose up -d
```
Access the Jupyter environment:
- Open your browser and navigate to http://localhost:8888
- The token is usually shown in the docker-compose logs. You can view them with:
```
docker-compose logs jupyter
```
Access RedisInsight:
- Open your browser and navigate to http://localhost:8001
- Connect to Redis using the following details:
  - Host: redis-java
  - Port: 6379
  - No password (unless configured)
When finished, stop the containers:
```
docker-compose down
```

Notebooks

Notebook	Description
RAG/spring_ai_redis_rag.ipynb	Demonstrates building a RAG-based beer recommendation chatbot using Spring AI and Redis as the vector store
semantic-routing/1_semantic_classification.ipynb	Demonstrates how to perform text classification with vector search (RedisVL) instead of LLMs
semantic-routing/2_semantic_tool_calling.ipynb	Demonstrates how to perform tool selection with vector search (RedisVL) instead of LLMs
semantic-routing/3_semantic_guardrails.ipynb	Demonstrates how to implement guardrails with vector search (RedisVL)
semantic-caching/1_pre_generated_semantic_caching.ipynb	Demonstrates how to perform pre generated semantic caching with RedisVL
semantic-caching/2_semantic_caching_with_langcache.ipynb	Demonstrates how to perform pre generated semantic caching with LangCache

Project Structure

notebooks/
├── .env                         # Environment variables (create this)
├── docker-compose.yml           # Docker Compose configuration
├── jupyter/                     # Jupyter configuration files
│   ├── Dockerfile               # Dockerfile for Jupyter with Java kernel
│   ├── environment.yml          # Conda environment specification
│   ├── install.py               # JJava kernel installation script
│   ├── kernel.json              # Kernel specification
│   └── java/                    # Java dependencies and configuration
│       └── pom.xml              # Maven project file with dependencies
└── resources/                   # Data files for notebooks
    └── beers.json.gz            # Compressed beer dataset

Implementation Details

Java Jupyter Kernel

The project uses JJava, a Jupyter kernel for Java based on JShell. This allows for interactive Java development in Jupyter notebooks.

Key components:

Java 21 for modern Java features
Maven for dependency management
JJava kernel for Jupyter integration

Spring AI Integration

The Spring AI notebooks showcase how to use Spring's AI capabilities with Redis:

Spring AI: Framework for building AI-powered applications
Redis Vector Store: Used for storing and querying vector embeddings
Transformer Models: For generating embeddings locally
RAG Pattern: Demonstrates the Retrieval Augmented Generation pattern

Docker Configuration

The Docker setup includes:

Jupyter Container:
- Based on minimal Jupyter notebook image
- Adds Java 21, Maven, and the JJava kernel
- Includes Python environment with PyTorch and other ML libraries
Redis Container:
- Uses Redis Stack image with Vector Search capabilities
- Persists data using Docker volumes
- Exposes Redis on port 6379 and RedisInsight on port 8001