Recipe for Vector Similarity Search with Redis OM Spring

java-recipes/README.md

@@ -1,5 +1,5 @@
 <div align="center">
-<div><img src="../assets/redis-logo.svg" style="width: 130px"> </div>
+<div><img src="../../assets/redis-logo.svg" style="width: 130px"> </div>
 <h1>Redis AI Java Resources</h1>
 <div align="center">
 
@@ -15,127 +15,25 @@
 <div></div>
 <br>
 
-[**Setup**](#setup) | [**Running the Project**](#running-the-project) | [**Notebooks**](#notebooks) | [**Project Structure**](#project-structure) | [**Implementation Details**](#implementation-details)
+[**Notebooks**](#notebooks) | [**Applications**](#applications) | [**Example Applications**](#example-applications)
 
 </div>
 <br>
 
-## 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
-- Redis Stack (includes Redis and RedisInsight)
-- Pre-installed dependencies for AI/ML workloads
-
-### Prerequisites
-
-- [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/)
-- OpenAI API key (for notebooks that use OpenAI services)
-
-### Environment Configuration
-
-1. Create a `.env` file in the project root with your OpenAI API key:
-
-```bash
-OPENAI_API_KEY=your_openai_api_key_here
-```
-
-## Running the Project
-
-1. Clone the repository (if you haven't already):
-
-   ```bash
-   git clone https://github.com/redis-developer/redis-ai-resources.git
-   cd redis-ai-resources/java-resources
-   ```
-
-2. Start the Docker containers:
-
-   ```bash
-   docker-compose up -d
-   ```
-
-3. Access the Jupyter environment:
-   - Open your browser and navigate to [http://localhost:8888](http://localhost:8888)
-   - The token is usually shown in the docker-compose logs. You can view them with:
-
-     ```bash
-     docker-compose logs jupyter
-     ```
-
-4. Access RedisInsight:
-   - Open your browser and navigate to [http://localhost:8001](http://localhost:8001)
-   - Connect to Redis using the following details:
-     - Host: redis-java
-     - Port: 6379
-     - No password (unless configured)
-
-5. When finished, stop the containers:
-
-   ```bash
-   docker-compose down
-   ```
-
 ## Notebooks
 
-| Notebook | Description |
-| --- | --- |
-| [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 |
-
-## Project Structure
-
-```bash
-java-recipes/
-├── .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](https://github.com/dflib/jjava), a Jupyter kernel for Java based on JShell. This allows for interactive Java development in Jupyter notebooks.
-
-Key components:
+| Notebook                                                                             | Description                                                                                                  |
+|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
+| [notebooks/RAG/spring_ai_redis_rag.ipynb](./notebooks/RAG/spring_ai_redis_rag.ipynb) | Demonstrates building a RAG-ba sed beer recommendation chatbot using Spring AI and Redis as the vector store |
 
-- Java 21 for modern Java features
-- Maven for dependency management
-- JJava kernel for Jupyter integration
+## Applications
 
-### Spring AI Integration
+| Application                                                                                                                 | Description                                                                                        |
+|-----------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
+| [applications/vector-similarity-search/spring_boot](./applications/vector-similarity-search/spring_boot_redis_om_spring.md) | Demonstrates building a vector similarity search application using Spring Boot and Redis OM Spring |
 
-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:
-
-1. **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
-
-2. **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
-
-## Example Applications
+## Example Notebooks & Applications
 
 ### Beer Recommendation Chatbot
 
@@ -146,3 +44,11 @@ The `spring-ai-rag.ipynb` notebook demonstrates:
 - Connecting to OpenAI for LLM capabilities
 - Building a RAG pipeline to answer beer-related queries
 - Semantic search over beer properties and descriptions
+
+### Vector Similarity Search with Redis OM Spring and Spring Boot
+
+The `spring_boot_redis_om_spring` directory contains a Spring Boot application that demonstrates how to use Redis OM Spring for vector similarity search. The application allows you to:
+- Add movies to the Redis database
+- Search for movies based on semantic similarity on the synopsis of the movie
+- Perform hybrid search by adding filters to genre, cast, and year 
+