A comprehensive educational repository demonstrating how to integrate various AI tools and services with Java applications.
This repository contains practical exercises and example implementations for working with AI APIs in Java, including:
- OpenAI Services: Text-to-speech, chat completions, image generation (DALL-E)
- Ollama Integration: Local AI model interactions with text and vision capabilities
- LangChain4j: Framework integration for AI-powered applications
- Retrieval-Augmented Generation (RAG): Document-based question answering
- Java 17 or higher
- Gradle 8.4+ (included via wrapper)
- OpenAI API key (for OpenAI services)
- Ollama installation (for local AI models)
-
OpenAI API Key: Set your OpenAI API key as an environment variable:
export OPENAI_API_KEY=your_openai_api_key_here -
Ollama Setup: Install Ollama and download the required models:
# Install Ollama from https://ollama.com ollama pull gemma3 ollama pull moondream # For vision tasks
./gradlew build./gradlew testNote: Some tests require external services (OpenAI API, Ollama) to be available and properly configured.
src/
├── main/java/com/kousenit/
│ ├── DalleService.java # DALL-E image generation
│ ├── EasyRAGDemo.java # RAG implementation example
│ ├── OllamaService.java # Ollama AI service integration
│ ├── OpenAiService.java # OpenAI API interactions
│ ├── TextToSpeechService.java # Audio generation from text
│ └── *Records.java # Data models for API interactions
└── test/java/com/kousenit/ # Comprehensive test suite
Generate MP3 audio files from text using OpenAI's TTS models:
- Models:
tts-1,tts-1-hd - Multiple voice options: alloy, echo, fable, onyx, nova, shimmer
Support for both cloud and local AI models:
- OpenAI: GPT-4.1-nano for advanced conversations
- Ollama: Local gemma3 model for privacy-focused applications
Image analysis and description generation:
- Multimodal support with Ollama's moondream model
- Base64 image encoding for API transmission
Create images from text descriptions using DALL-E 3:
- High-quality image generation
- Customizable size, quality, and style parameters
RAG implementation for document-based question answering:
- LangChain4j integration
- Vector store support for efficient document retrieval
- LangChain4j: 1.0.1 (AI framework)
- Apache POI: 5.4.1 (Document processing)
- Gson: 2.13.1 (JSON parsing)
- JUnit: 5.13.0 (Testing)
- Spotless: 7.0.4 (Code formatting)
Detailed step-by-step exercises are available in labs.md, covering:
- Generate Audio from Text - OpenAI TTS integration
- List OpenAI Models - API exploration and model discovery
- Install and Use Ollama - Local AI model setup
- Streaming Responses - Real-time AI interactions
- Vision Requests - Image analysis capabilities
- Conversation Management - Multi-turn chat implementations
- Image Generation - DALL-E integration
This repository serves as a practical guide for:
- Understanding AI API integration patterns
- Learning modern Java features (records, sealed interfaces, pattern matching)
- Implementing error handling for external services
- Working with different AI model types (text, vision, audio)
- Building production-ready AI applications
This is an educational repository. The code demonstrates various AI integration patterns and serves as reference implementations for learning purposes.
MIT License - see LICENSE file for details.