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Adds self-contained docker environment for java-recipes
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‎.gitignore‎

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libs/redis/docs/.Trash*
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.python-version
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.idea/*
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java-recipes/.*

‎java-recipes/RAG/spring_ai_redis_rag.ipynb‎

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‎java-recipes/README.md‎

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<div align="center">
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<div><img src="../assets/redis-logo.svg" style="width: 130px"> </div>
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<h1>Redis AI Java Resources</h1>
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<div align="center">
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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![Java](https://img.shields.io/badge/Java-21-orange)
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![Spring AI](https://img.shields.io/badge/Spring%20AI-1.0.0--M6-green)
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</div>
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<div>
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✨ Java-based code examples, notebooks, and resources for using Redis in AI and ML applications. ✨
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</div>
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<div></div>
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<br>
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[**Setup**](#setup) | [**Running the Project**](#running-the-project) | [**Notebooks**](#notebooks) | [**Project Structure**](#project-structure) | [**Implementation Details**](#implementation-details)
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</div>
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<br>
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## Setup
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This project uses Docker Compose to set up a complete environment for running Java-based AI applications with Redis. The environment includes:
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- A Jupyter Notebook server with Java kernel support
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- Redis Stack (includes Redis and RedisInsight)
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- Pre-installed dependencies for AI/ML workloads
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### Prerequisites
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- [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/)
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- OpenAI API key (for notebooks that use OpenAI services)
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### Environment Configuration
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1. Create a `.env` file in the project root with your OpenAI API key:
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```
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OPENAI_API_KEY=your_openai_api_key_here
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```
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## Running the Project
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1. Clone the repository (if you haven't already):
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```bash
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git clone https://github.com/redis-developer/redis-ai-resources.git
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cd redis-ai-resources/java-resources
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```
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2. Start the Docker containers:
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```bash
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docker-compose up -d
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```
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3. Access the Jupyter environment:
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- Open your browser and navigate to [http://localhost:8888](http://localhost:8888)
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- The token is usually shown in the docker-compose logs. You can view them with:
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```bash
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docker-compose logs jupyter
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```
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4. Access RedisInsight:
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- Open your browser and navigate to [http://localhost:8001](http://localhost:8001)
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- Connect to Redis using the following details:
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- Host: redis-java
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- Port: 6379
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- No password (unless configured)
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5. When finished, stop the containers:
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```bash
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docker-compose down
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```
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## Notebooks
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| Notebook | Description |
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| --- | --- |
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| [RAG/spring_ai_redis_rag.ipynb](./RAG/spring_ai_redis_rag.ipynb) | Demonstrates building a RAG-based beer recommendation chatbot using Spring AI and Redis as the vector store |
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## Project Structure
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```
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java-resources/
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├── .env # Environment variables (create this)
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├── docker-compose.yml # Docker Compose configuration
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├── jupyter/ # Jupyter configuration files
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│ ├── Dockerfile # Dockerfile for Jupyter with Java kernel
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│ ├── environment.yml # Conda environment specification
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│ ├── install.py # JJava kernel installation script
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│ ├── kernel.json # Kernel specification
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│ └── java/ # Java dependencies and configuration
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│ └── pom.xml # Maven project file with dependencies
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└── resources/ # Data files for notebooks
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└── beers.json.gz # Compressed beer dataset
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```
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## Implementation Details
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### Java Jupyter Kernel
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The project uses [JJava](https://github.com/dflib/jjava), a Jupyter kernel for Java based on JShell. This allows for interactive Java development in Jupyter notebooks.
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Key components:
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- Java 21 for modern Java features
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- Maven for dependency management
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- JJava kernel for Jupyter integration
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### Spring AI Integration
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The Spring AI notebooks showcase how to use Spring's AI capabilities with Redis:
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- **Spring AI**: Framework for building AI-powered applications
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- **Redis Vector Store**: Used for storing and querying vector embeddings
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- **Transformer Models**: For generating embeddings locally
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- **RAG Pattern**: Demonstrates the Retrieval Augmented Generation pattern
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### Docker Configuration
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The Docker setup includes:
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1. **Jupyter Container**:
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- Based on minimal Jupyter notebook image
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- Adds Java 21, Maven, and the JJava kernel
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- Includes Python environment with PyTorch and other ML libraries
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2. **Redis Container**:
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- Uses Redis Stack image with Vector Search capabilities
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- Persists data using Docker volumes
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- Exposes Redis on port 6379 and RedisInsight on port 8001
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## Example Applications
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### Beer Recommendation Chatbot
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The `spring-ai-rag.ipynb` notebook demonstrates:
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- Loading and embedding beer data into Redis Vector Store
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- Using local transformer models for generating embeddings
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- Connecting to OpenAI for LLM capabilities
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- Building a RAG pipeline to answer beer-related queries
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- Semantic search over beer properties and descriptions

‎java-recipes/docker-compose.yml‎

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name: redis-ai-java
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services:
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jupyter:
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build:
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context: .
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dockerfile: ./jupyter/Dockerfile
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ports:
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- "8888:8888"
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environment:
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- JUPYTER_ENABLE_LAB=yes
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env_file:
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- .env
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volumes:
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- ./:/home/jovyan/
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- ./resources:/home/jovyan/resources
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redis-java:
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image: redis/redis-stack:latest
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ports:
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- "6379:6379" # Redis database port
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- "8001:8001" # RedisInsight port
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volumes:
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- redis-data:/data # Persist Redis data
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volumes:
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redis-data:

‎java-recipes/jupyter/Dockerfile‎

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FROM quay.io/jupyter/minimal-notebook:latest
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RUN mkdir /home/jovyan/resources
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USER root
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WORKDIR /home/jovyan
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# Install dependencies: Java 21 and Maven
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RUN apt-get update && apt-get install -y openjdk-21-jdk maven
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# Copy the pre-created Maven project and jjava-glue project
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COPY ./jupyter/java /home/jovyan/java
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COPY ./jupyter/install.py /home/jovyan/install.py
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# Use Maven to download dependencies for JJava
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WORKDIR /home/jovyan/java
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# Download the JJava jar directly
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RUN mvn dependency:get -Dartifact=org.dflib.jjava:jjava:1.0-M3 -Ddest=./ -Dtransitive=false
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RUN mv jjava-1.0-M3.jar jjava.jar
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# Pre-download Spring AI Dependencies
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RUN mvn dependency:get -Dartifact=org.springframework.ai:spring-ai-openai:1.0.0-M6
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RUN mvn dependency:get -Dartifact=org.springframework.ai:spring-ai-transformers:1.0.0-M6
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RUN mvn dependency:get -Dartifact=org.springframework.ai:spring-ai-redis-store:1.0.0-M6
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# Pre-download Jedis
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RUN mvn dependency:get -Dartifact=redis.clients:jedis:5.2.0
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# Download all dependencies
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RUN mvn dependency:copy-dependencies -DoutputDirectory=./lib
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# Create a list of dependencies for the classpath
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RUN find ./lib -name "*.jar" | tr '\n' ':' > classpath.txt
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# Add the jjava.jar to the classpath
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RUN echo -n "/home/jovyan/java/jjava.jar:" >> classpath.txt
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# Install the kernel with classpath configuration
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WORKDIR /home/jovyan
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RUN python install.py --prefix /opt/conda/ --classpath $(cat /home/jovyan/java/classpath.txt)
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# Pre-download Transformer Models
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RUN pip install transformers torch
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RUN mkdir -p /home/jovyan/.cache/huggingface/hub
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# Pre-download the specific model used in Spring AI Transformers
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RUN python -c "from transformers import AutoModel; AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')"
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# Clean up Maven artifacts but keep the jjava.jar and lib directory
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RUN rm -rf /home/jovyan/java/target /home/jovyan/java/.m2 /home/jovyan/java/pom.xml \
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/home/jovyan/java/classpath.txt \
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&& rm -f /home/jovyan/install.py
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# Install conda packages from environment.yml
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COPY ./jupyter/environment.yml /tmp/
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RUN conda env update -f /tmp/environment.yml && \
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conda clean --all -f -y && \
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fix-permissions "${CONDA_DIR}" && \
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fix-permissions "/home/${NB_USER}"
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WORKDIR /home/jovyan
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USER $NB_UID

‎java-recipes/jupyter/environment.yml‎

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name: base
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channels:
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- pytorch
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- conda-forge
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- defaults
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dependencies:
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- pytorch
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- torchtext
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- gensim

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