Spring AI ELT Vector Store: How to store documents in Chroma

Overview

A reference project that demonstrates how to:

• build a Spring Boot 3.5 application on Java 21
• read pdf documents from local storage
• transform it based on the TokenTextSplitter
• call OpenAI models through spring-ai
• store and retrieve embeddings in Chroma
• read and chunk documents with the Pdf Reader document reader
• expose simple REST end-points for data ingestion

Tech Stack

• Spring Boot
• Spring AI
• OpenAI Integration
• Chroma as Vector Store
• Java 21
• Docker
• Maven

Prerequisites

• JDK 21
• Maven
• OpenAI API key
• IDE (IntelliJ IDEA, Eclipse, or VS Code)

2 Configuration

All secrets are read from environment variables or the .env file in the project root (use the provided .env.template as a starting point):

Variable	Purpose
OPENAI_API_KEY	key for OpenAI completions / chat

Example:

bash
# .env
OPENAI_API_KEY=sk-********************************
API_NINJAS_KEY=ninjas_********************************

Quick Start

Installation

1. Clone the repository:

git clone [repository-url]
cd spring-ai-etl-vector-store

2. Configure OpenAI: Create application.properties in src/main/resources/ and add:

spring.ai.openai.api-key=your-api-key-here

3. Build and run:

docker-compose up

mvn clean install
mvn spring-boot:run

API Usage

Get Capital City

Endpoint: GET /api/v1/etl/run-ingestion

Project Structure

spring-ai-etl-vecgtor-store/
├── src/
│   ├── main/
│   │   ├── java/
│   │   └── resources/
│   └── test/
└── pom.xml

Development

Building

mvn clean install

Running Tests

mvn test

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Support

For support and questions, please open an issue in the repository.